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A survey on computer vision approaches for automated classification of skin diseases

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Abstract

Skin diseases are a significant concern for public health, demanding accurate diagnosis for effective treatment. However, traditional diagnostic methods often suffer from subjectivity, invasiveness, and resource intensiveness. In recent years, computer vision techniques have emerged as promising solutions, offering auto- mated tools for skin disease diagnosis that can improve accuracy, efficiency, and accessibility. In this paper, we provide a comprehensive review of computer vision approaches for the automated diagnosis of various skin diseases. We delve into image preprocessing techniques, feature extraction methods, classification algo- rithms, and evaluation metrics commonly employed in this domain. Additionally, we examine how computer vision is applied in specific skin diseases, such as melanoma, psoriasis, eczema, and fungal infections. We also discuss the challenges faced in this field and suggest future directions for research. In summary, this paper highlights how computer vision holds the potential to transform the land- scape of skin disease diagnosis, emphasizing the continued necessity for research and innovation in this domain.

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References 

  1. James WD, Elston D, Berger T (2011) Andrew’s Diseases of the Skin E-book: Clinical Dermatology. Elsevier Health Sciences, USA

    Google Scholar 

  2. Seth D, Cheldize K, Brown D, Freeman EE (2017) Global burden of skin disease: inequities and innovations. Current dermatology reports 6:204–210

    Article  Google Scholar 

  3. Yakupu A, Aimaier R, Yuan B, Chen B, Cheng J, Zhao Y, Peng Y, Dong J, Lu S (2023) The burden of skin and subcutaneous diseases: findings from the global burden of disease study 2019. Front Public Health 11:1145513

    Article  Google Scholar 

  4. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S (2017) Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639):115–118

    Article  Google Scholar 

  5. Pham T-C, Luong C-M, Hoang V-D, Doucet A (2021) Ai outperformed every dermatologist in dermoscopic melanoma diagnosis, using an optimized deep-cnn architecture with custom mini-batch logic and loss function. Sci Rep 11(1):17485

    Article  Google Scholar 

  6. Maron RC, Weichenthal M, Utikal JS, Hekler A, Berking C, Hauschild A, Enk AH, Haferkamp S, Klode J, Schadendorf D et al (2019) Systematic outperformance of 112 dermatologists in multiclass skin cancer image classifica- tion by convolutional neural networks. Eur J Cancer 119:57–65

    Article  Google Scholar 

  7. Haenssle HA, Fink C, Schneiderbauer R, Toberer F, Buhl T, Blum A, Kalloo A, Hassen ABH, Thomas L, Enk A et al (2018) Man against machine: diagnostic performance of a deep learning convolutional neural network for der- moscopic melanoma recognition in comparison to 58 dermatologists. Ann Oncol 29(8):1836–1842

    Article  Google Scholar 

  8. Rashid MS, Gilanie G, Naveed S, Cheema S, Sajid M (2024) Automated detection and classification of psoriasis types using deep neural networks from dermatology images. SIViP 18(1):163–172

    Article  Google Scholar 

  9. Hammad M, P-lawiak P, ElAffendi M, El-Latif AAA, Latif AAA (2023) Enhanced deep learning approach for accurate eczema and psoriasis skin detection. Sensors 23(16):7295

    Article  Google Scholar 

  10. Nigat TD, Sitote TM, Gedefaw BM (2023) Fungal Skin Disease Classification Using the Convolutional Neural Network. J Healthcare Eng 2023:6370416. https://doi.org/10.1155/2023/6370416

  11. Bajwa MN, Muta K, Malik MI, Siddiqui SA, Braun SA, Homey B, Dengel A, Ahmed S (2020) Computer-aided diagnosis of skin diseases using deep neural networks. Appl Sci 10(7):2488

    Article  Google Scholar 

  12. Chan S, Reddy V, Myers B, Thibodeaux Q, Brownstone N, Liao W (2020) Machine learning in dermatology: current applications, opportunities, and limitations. Dermatology and therapy 10:365–386

    Article  Google Scholar 

  13. Liopyris K, Gregoriou S, Dias J, Stratigos AJ (2022) Artificial intelligence in dermatology: challenges and perspectives. Dermatology and Therapy 12(12):2637–2651

    Article  Google Scholar 

  14. Burlando M, Muracchioli A, Cozzani E, Parodi A (2021) Psoriasis, vitiligo, and biologic therapy: Case report and narrative review. Case Rep Dermatol 13(2):372–378

    Article  Google Scholar 

  15. Haenssle HA, Fink C, Toberer F, Winkler J, Stolz W, Deinlein T, Hofmann-Wellenhof R, Lallas A, Emmert S, Buhl T et al (2020) Man against machine reloaded: performance of a market-approved convolutional neural net- work in classifying a broad spectrum of skin lesions in comparison with 96 dermatologists working under less artificial conditions. Ann Oncol 31(1):137–143

    Article  Google Scholar 

  16. Pathania YS, Apalla Z, Salerni G, Patil A, Grabbe S, Goldust M (2022) Non- invasive diagnostic techniques in pigmentary skin disorders and skin cancer. J Cosmet Dermatol 21(2):444–450

    Article  Google Scholar 

  17. Rattan, P., Kumari, A.: Systematic review: An early detection of skin disease using machine learning. Data-Centric AI Solutions and Emerging Technologies in the Healthcare Ecosystem, 241–262

  18. Joseph S, Olugbara OO (2022) Preprocessing effects on performance of skin lesion saliency segmentation. Diagnostics 12(2):344. https://doi.org/10.3390/diagnostics12020344

  19. Schaefer G, Rajab MI, Celebi ME, Iyatomi H (2011) Colour and contrast enhancement for improved skin lesion segmentation. Comput Med Imaging Graph 35(2):99–104

    Article  Google Scholar 

  20. Deepa J, Madhavan P (2023) Abt-gamnet: A novel adaptive boundary-aware trans- former with gated attention mechanism for automated skin lesion segmentation. Biomed Signal Process Control 84:104971

    Article  Google Scholar 

  21. Goceri E (2023) Evaluation of denoising techniques to remove speckle and gaussian noise from dermoscopy images. Comput Biol Med 152:106474

    Article  Google Scholar 

  22. Bansal N, Sridhar S (2024) Hexa-gan: Skin lesion image inpainting via hexagonal sampling based generative adversarial network. Biomed Signal Process Control 89:105603

    Article  Google Scholar 

  23. Sengupta S, Mittal N, Modi M (2020) Improved skin lesions detection using color space and artificial intelligence techniques. J Dermatol Treat 31(5):511–518

    Article  Google Scholar 

  24. Sengupta S, Mittal N, Modi M (2019) Segmentation of skin lesion images using fudge factor based techniques. In: Advances in Interdisciplinary Engineering: Select Proceedings of FLAME 2018. Springer, Singapore, pp 837-846

  25. Saiwaeo S, Mungmai L, Preedalikit W, Arwatchananukul S, Aunsri N (2022) A comparative study of image enhancement methods for human skin image. In: 2022 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT & NCON) pp 484–488. IEEE

  26. Salvi M, Branciforti F, Veronese F, Zavattaro E, Tarantino V, Savoia P, Meiburger KM (2022) Dermocc-gan: a new approach for standardizing derma- tological images using generative adversarial networks. Comput Methods Programs Biomed 225:107040

    Article  Google Scholar 

  27. Ghorbani A, Natarajan V, Coz D, Liu Y (2020) DermGAN: synthetic generation of clinical skin images with pathology. In: Machine Learning for Health Workshop. PMLR, pp 155–170

  28. Khan MA, Akram T, Sharif M, Saba T, Javed K, Lali IU, Tanik UJ, Rehman A (2019) Construction of saliency map and hybrid set of features for efficient segmentation and classification of skin lesion. Microsc Res Tech 82(6):741–763

    Article  Google Scholar 

  29. Pereira PM, Tavora LM, Fonseca-Pinto R, Paiva RP, Assunção PAA, de Faria SM (2019) Image segmentation using gradient-based histogram thresholding for skin lesion delineation. Bioimaging, pp 84–91

  30. Namboodiri TS, Jayachandran A (2020) Multi-class skin lesions classification sys- tem using probability map based region growing and dcnn. Int J Comput Intel Syst 13(1):77–84

    Article  Google Scholar 

  31. Riaz F, Naeem S, Nawaz R, Coimbra M (2018) Active contours based seg- mentation and lesion periphery analysis for characterization of skin lesions in dermoscopy images. IEEE J Biomed Health Inform 23(2):489–500

    Article  Google Scholar 

  32. Bama S, Velumani R, Prakash N, Hemalakshmi G, Mohanarathinam A (2021) Automatic segmentation of melanoma using superpixel region growing technique. Materials Today: Proceedings 45:1726–1732

    Google Scholar 

  33. Nancy VAO, Rajasekar V, Arya MS (2023) Skin lesion segmentation and classification using fcn-alexnet framework. J Theor Appl Inf Technol 101(24)

  34. Tamoor M, Naseer A, Khan A, Zafar K (2023) Skin lesion segmentation using an ensemble of different image processing methods. Diagnostics 13(16):2684

    Article  Google Scholar 

  35. Zhou L, Liang L, Sheng X (2023) Ga-net: Ghost convolution adaptive fusion skin lesion segmentation network. Comput Biol Med 164:107273

    Article  Google Scholar 

  36. Zhao C, Shuai R, Ma L, Liu W, Wu M (2022) Segmentation of skin lesions image based on u-net++. Multimed Tools Appl 81(6):8691–8717

    Article  Google Scholar 

  37. Yu Z, Yu L, Zheng W, Wang S (2023) EIU-Net: enhanced feature extraction and improved skip connections in U-Net for skin lesion segmentation. Comput Biol Med 107081

  38. Dash M, Londhe ND, Ghosh S, Semwal A, Sonawane RS (2019) Pslsnet: Automated psoriasis skin lesion segmentation using modified u-net-based fully convolutional network. Biomed Signal Process Control 52:226–237

    Article  Google Scholar 

  39. Garcia-Arroyo JL, Garcia-Zapirain B (2019) Segmentation of skin lesions in der- moscopy images using fuzzy classification of pixels and histogram thresholding. Comput Methods Programs Biomed 168:11–19

    Article  Google Scholar 

  40. Mishra NK, Kaur R, Kasmi R, Hagerty JR, LeAnder R, Stanley RJ, Moss RH, Stoecker WV (2019) Automatic lesion border selection in dermoscopy images using morphology and color features. Skin Research and Technology 25(4):544–552

    Article  Google Scholar 

  41. Bansal P, Garg R, Soni P (2022) Detection of melanoma in dermoscopic images by integrating features extracted using handcrafted and deep learning models. Comput Ind Eng 168:108060

    Article  Google Scholar 

  42. Arasi MA, El-Horbaty ESM, El-Sayed A (2018) Classification of dermoscopy images using naive bayesian and decision tree techniques. In: 2018 1st Annual International Conference on Information and Sciences (AiCIS). IEEE, pp 7–12

  43. Javed R, Rahim MSM, Saba T, Rehman A (2020) A comparative study of features selection for skin lesion detection from dermoscopic images. Network Model Analys Health Inform Bioinform 9(1):4

    Article  Google Scholar 

  44. Senan EM, Jadhav ME (2020) Techniques for the detection of skin lesions in PH 2 dermoscopy images using local binary pattern (LBP). In: International Conference on Recent Trends in Image Processing and Pattern Recognition Singapore, vol 1381. Springer, Berlin/Heidelberg, Germany, pp 14–25

  45. Seeja R, Suresh A (2019) Deep learning based skin lesion segmentation and classifi- cation of melanoma using support vector machine (svm). Asian Pacific J Cancer Prevention: APJCP 20(5):1555

    Article  Google Scholar 

  46. Yao P, Shen S, Xu M, Liu P, Zhang F, Xing J, Shao P, Kaffenberger B, Xu RX (2021) Single model deep learning on imbalanced small datasets for skin lesion classification. IEEE Trans Med Imaging 41(5):1242–1254

    Article  Google Scholar 

  47. Mahbod A, Schaefer G, Wang C, Ecker R, Ellinge I (2019) Skin lesion classification using hybrid deep neural networks. In: ICASSP 2019-2019 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 1229–1233

  48. Midasala VD, Prabhakar B, Chaitanya JK, Sirnivas K, Eshwar D, Kumar PM (2024) Mfeuslnet: Skin cancer detection and classification using inte- grated ai with multilevel feature extraction-based unsupervised learning. Eng Sci Technol Int J 51:101632

    Google Scholar 

  49. Alsahafi YS, Kassem MA, Hosny KM (2023) Skin-net: A novel deep residual network for skin lesions classification using multilevel feature extraction and cross-channel correlation with detection of outlier. J Big Data 10(1):105

    Article  Google Scholar 

  50. Gajera HK, Zaveri MA, Nayak DR (2022) Patch-based local deep feature extrac- tion for automated skin cancer classification. Int J Imaging Syst Technol 32(5):1774–1788

    Article  Google Scholar 

  51. Solatidehkordi Z, Zualkernan I (2022) Survey on recent trends in medical image classification using semi-supervised learning. Appl Sci 12(23):12094

    Article  Google Scholar 

  52. Sekhar KSR, Babu TR, Prathibha G, Vijay K, Ming LC (2021) Dermoscopic image classification using cnn with handcrafted features. Journal of king Saud University-science 33(6):101550

    Article  Google Scholar 

  53. Sharafudeen M, Vinod Chandra SS (2023) Detecting skin lesions fusing handcrafted features in image network ensembles. Multimedia Tools and Applications 82(2):3155–3175

    Article  Google Scholar 

  54. Vidhyalakshmi AM, Kanchana M (2024) Classification of skin disease using a novel hybrid flash butterfly optimization from dermoscopic images. Neural Comput Appl 36(8):4311–4324

    Article  Google Scholar 

  55. Thanka MR, Edwin EB, Ebenezer V, Sagayam KM, Reddy BJ, Gu¨nerhan H, Emadifar H (2023) A hybrid approach for melanoma classifica- tion using ensemble machine learning techniques with deep transfer learning. Computer Methods and Programs in Biomedicine Update 3:100103

    Article  Google Scholar 

  56. Daghrir J, Tlig L, Bouchouicha M, Sayadi M (2020) Melanoma skin cancer detection using deep learning and classical machine learning techniques: a hybrid approach. In: Proceedings of the 2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), Sfax, Tunisia, 2–5. IEEE, Manhattan, NY, USA, pp 1–5

  57. Kusuma S, Vasundharadevi G, Abhinay Kanth DM (2022) A hybrid model for skin disease classification using transfer learning. In: 2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT), pp 1093–1096

  58. Adegun AA, Viriri S (2020) Fcn-based densenet framework for automated detection and classification of skin lesions in dermoscopy images. IEEE Access 8:150377–150396

    Article  Google Scholar 

  59. Karthik R, Vaichole TS, Kulkarni SK, Yadav O, Khan F (2022) Eff2net: An efficient channel attention-based convolutional neural network for skin disease classification. Biomed Signal Process Control 73:103406

    Article  Google Scholar 

  60. Lucius M, De All J, De All JA, Belvisi M, Radizza L, Lanfranconi M, Lorenzatti V, Galmarini CM (2020) Deep neural frameworks improve the accuracy of general practitioners in the classification of pigmented skin lesions. Diagnostics 10(11):969

    Article  Google Scholar 

  61. Nugroho AK, Wardoyo R, Wibowo ME, Soebono H (2024) Image dermoscopy skin lesion classification using deep learning method: systematic literature review. Bulletin Elect Eng Inform 13(2):1042–1049

    Article  Google Scholar 

  62. Li L-F, Wang X, Hu W-J, Xiong NN, Du Y-X, Li B-S (2020) Deep learning in skin disease image recognition: A review. Ieee Access 8:208264–208280

    Article  Google Scholar 

  63. Choudhary P, Singhai J, Yadav J (2022) Skin lesion detection based on deep neural networks. Chemom Intell Lab Syst 230:104659

    Article  Google Scholar 

  64. Serte S, Demirel H (2019) Gabor wavelet-based deep learning for skin lesion classification. Comput Biol Med 113:103423

    Article  Google Scholar 

  65. Ünver HM, Ayan E (2019) Skin lesion segmentation in dermoscopic images with combination of yolo and grabcut algorithm. Diagnostics 9(3):72

    Article  Google Scholar 

  66. Tang P, Liang Q, Yan X, Xiang S, Sun W, Zhang D, Coppola G (2019) Efficient skin lesion segmentation using separable-unet with stochastic weight averaging. Comput Methods Programs Biomed 178:289–301

    Article  Google Scholar 

  67. Hosny KM, Kassem MA, Foaud MM (2019) Classification of skin lesions using transfer learning and augmentation with alex-net. PLoS ONE 14(5):0217293

    Article  Google Scholar 

  68. Gonzalez-Diaz I (2018) Dermaknet: Incorporating the knowledge of dermatologists to convolutional neural networks for skin lesion diagnosis. IEEE J Biomed Health Inform 23(2):547–559

    Article  Google Scholar 

  69. Albahar MA (2019) Skin lesion classification using convolutional neural network with novel regularizer. IEEE Access 7:38306–38313

    Article  Google Scholar 

  70. Hagerty JR, Stanley RJ, Almubarak HA, Lama N, Kasmi R, Guo P, Drugge RJ, Rabinovitz HS, Oliviero M, Stoecker WV (2019) Deep learn- ing and handcrafted method fusion: higher diagnostic accuracy for melanoma dermoscopy images. IEEE J Biomed Health Inform 23(4):1385–1391

    Article  Google Scholar 

  71. Zhang J, Xie Y, Xia Y, Shen C (2019) Attention residual learning for skin lesion classification. IEEE Trans Med Imaging 38(9):2092–2103

    Article  Google Scholar 

  72. Akram T, Lodhi HMJ, Naqvi SR, Naeem S, Alhaisoni M, Ali M, Haider SA, Qadri NN (2020) A multilevel features selection framework for skin lesion classification. HCIS 10:1–26

    Google Scholar 

  73. Song L, Lin J, Wang ZJ, Wang H (2020) An end-to-end multi-task deep learn- ing framework for skin lesion analysis. IEEE J Biomed Health Inform 24(10):2912–2921

    Article  Google Scholar 

  74. Wei L, Ding K, Hu H (2020) Automatic skin cancer detection in dermoscopy images based on ensemble lightweight deep learning network. IEEE Access 8:99633–99647

    Article  Google Scholar 

  75. Kadampur MA, Al Riyaee S (2020) Skin cancer detection: Applying a deep learning based model driven architecture in the cloud for classifying dermal cell images. Informatics in Medicine Unlocked 18:100282

    Article  Google Scholar 

  76. Wang X, Jiang X, Ding H, Liu J (2019) Bi-directional dermoscopic feature learn- ing and multi-scale consistent decision fusion for skin lesion segmentation. IEEE Trans Image Process 29:3039–3051

    Article  Google Scholar 

  77. Pacheco AG, Krohling RA (2020) The impact of patient clinical information on automated skin cancer detection. Comput Biol Med 116:103545

    Article  Google Scholar 

  78. Goyal M, Oakley A, Bansal P, Dancey D, Yap MH (2019) Skin lesion segmen- tation in dermoscopic images with ensemble deep learning methods. Ieee Access 8:4171–4181

    Article  Google Scholar 

  79. Hasan MK, Dahal L, Samarakoon PN, Tushar FI, Martí R (2020) Dsnet: Auto- matic dermoscopic skin lesion segmentation. Computers in biology and medicine 120:103738

    Article  Google Scholar 

  80. Xie Y, Zhang J, Xia Y, Shen C (2020) A mutual bootstrapping model for auto- mated skin lesion segmentation and classification. IEEE Trans Med Imaging 39(7):2482–2493

    Article  Google Scholar 

  81. Gessert N, Sentker T, Madesta F, Schmitz R, Kniep H, Baltruschat I, Werner R, Schlaefer A (2019) Skin lesion classification using cnns with patch-based attention and diagnosis-guided loss weighting. IEEE Trans Biomed Eng 67(2):495–503

    Article  Google Scholar 

  82. Khan MA, Muhammad K, Sharif M, Akram T, Albuquerque VHC (2021) Multi-class skin lesion detection and classification via teledermatology. IEEE J Biomed Health Inform 25(12):4267–4275

    Article  Google Scholar 

  83. Zhang B, Wang Z, Gao J, Rutjes C, Nufer K, Tao D, Feng DD, Menzies SW (2020) Short-term lesion change detection for melanoma screening with novel siamese neural network. IEEE Trans Med Imaging 40(3):840–851

    Article  Google Scholar 

  84. Leite M, Parreira WD, Fernandes AMDR, Leithardt VRQ (2022) Image segmentation for human skin detection. Applied Sciences 12(23):12140

    Article  Google Scholar 

  85. Ahammed M, Al Mamun M, Uddin MS (2022) A machine learning approach for skin disease detection and classification using image segmentation. Healthcare Analytics 2:100122

    Article  Google Scholar 

  86. Nauta M, Walsh R, Dubowski A, Seifert C (2021) Uncovering and correct- ing shortcut learning in machine learning models for skin cancer diagnosis. Diagnostics 12(1):40

    Article  Google Scholar 

  87. Wang S, Yin Y, Wang D, Wang Y, Jin Y (2021) Interpretability-based multi- modal convolutional neural networks for skin lesion diagnosis. IEEE transactions on cybernetics 52(12):12623–12637

    Article  Google Scholar 

  88. Ain QU, Al-Sahaf H, Xue B, Zhang M (2022) Automatically diagnosing skin cancers from multimodality images using two-stage genetic programming. IEEE Transactions on Cybernetics 53(5):2727–2740

    Article  Google Scholar 

  89. Li X, Desrosiers C, Liu X (2022) Deep neural forest for out-of-distribution detection of skin lesion images. IEEE J Biomed Health Inform 27(1):157–165

    Article  Google Scholar 

  90. Liu Z, Xiong R, Jiang T (2022) Ci-net: clinical-inspired network for automated skin lesion recognition. IEEE Trans Med Imaging 42(3):619–632

    Article  Google Scholar 

  91. Yue G, Wei P, Zhou T, Jiang Q, Yan W, Wang T (2022) Toward multicenter skin lesion classification using deep neural network with adaptively weighted balance loss. IEEE Trans Med Imaging 42(1):119–131

    Article  Google Scholar 

  92. Lee K, Cavalcanti TC, Kim S, Lew HM, Suh DH, Lee DH, Hwang JY (2022) Multi-task and few-shot learning-based fully automatic deep learning plat- form for mobile diagnosis of skin diseases. IEEE J Biomed Health Inform 27(1):176–187

    Article  Google Scholar 

  93. Cao W, Yuan G, Liu Q, Peng C, Xie J, Yang X, Ni X, Zheng J (2022) Icl- net: Global and local inter-pixel correlations learning network for skin lesion segmentation. IEEE J Biomed Health Inform 27(1):145–156

    Article  Google Scholar 

  94. Baig AR, Abbas Q, Almakki R, Ibrahim ME, AlSuwaidan L, Ahmed AE (2023) Light-dermo: A lightweight pretrained convolution neural network for the diagnosis of multiclass skin lesions. Diagnostics 13(3):385

    Article  Google Scholar 

  95. Gupta C, Gondhi NK, Lehana PK (2019) Analysis and identification of dermato- logical diseases using gaussian mixture modeling. IEEE Access 7:99407–99427

    Article  Google Scholar 

  96. Lόpez-Leyva JA, Guerra-Rosas E, Álvarez-Borrego J (2021) Multi-class diagnosis of skin lesions using the fourier spectral information of images on additive color model by artificial neural network. IEEE Access 9:35207–35216

    Article  Google Scholar 

  97. Okuboyejo DA, Olugbara OO (2022) Classification of skin lesions using weighted majority voting ensemble deep learning. Algorithms 15(12):443

    Article  Google Scholar 

  98. Raj R, Londhe ND, Sonawane R (2023) Pslsnetv2: End to end deep learning system for measurement of area score of psoriasis regions in color images. Biomed Signal Process Control 79:104138

    Article  Google Scholar 

  99. Yang Y, Wang J, Xie F, Liu J, Shu C, Wang Y, Zheng Y, Zhang H (2021) A convolutional neural network trained with dermoscopic images of psoriasis performed on par with 230 dermatologists. Comput Biol Med 139:104924

    Article  Google Scholar 

  100. Roslan RB, Razly INM, Sabri N, Ibrahim Z (2020) Evaluation of psoriasis skin disease classification using convolutional neural network. IAES Int J Artif Intel 9(2):349

    Google Scholar 

  101. Li Y, Zhao D, Xu Z, Heidari AA, Chen H, Jiang X, Liu Z, Wang M, Zhou Q, Xu S (2023) bsrwpso-fknn: A boosted pso with fuzzy k-nearest neighbor classifier for predicting atopic dermatitis disease. Front Neuroinform 16:1063048

    Article  Google Scholar 

  102. Pan K, Hurault G, Arulkumaran K, Williams HC, Tanaka RJ (2020) EczemaNet: automating detection and severity assessment of atopic dermatitis. Springer International Publishing, Berlin/Heidelberg, Germany,  pp 220–230

  103. Gökbay IZ, Zileli ZB, Pelin S, Aksoy TT, Yarman S (2019) A linear stochas- tic system approach to model symptom based clinical decision support tool for the early diagnosis for psoriasis, seborrheic dermatitis, rosacea and chronic dermatitis. Electrica 19(1):48–58

    Article  Google Scholar 

  104. Jain A, Saini ML, Saklani A, Biju A (2023) Tinea-corporis skin disease detection using CNN and kernel SVM. In: 2023 3rd International Conference on Technological Advancements in Computational Sciences (ICTACS), pp 157–161

  105. Nimesh V, Weerasinghe R (2021) Differential diagnosis of ringworm and eczema using image processing and deep learning. In: 2021 21st International Conference on Advances in ICT for Emerging Regions (ICter), 147–152. IEEE

  106. Negi Y, Marimuthu P, Rauniyar NR, Patil US, Shaheen H (2023) Herpes zoster identification using optimized deep neural network. In: International Conference on Computer & Communication Technologies, pp 361–370

  107. Bascil MS (2019) Convolutional neural network to extract the best treatment way of warts based on data mining. Rev d’Intelligence Artif 33:165–70. https://doi.org/10.18280/ria.330301

  108. Nikam KR (2022) Early stage diagnosis of eye herpes (NAGIN) by machine learning and image processing technique: detection and recognition of eye herpes (NAGIN) by using CAD system analysis. In: Research Anthology on Machine Learning Techniques, Methods, and Applications, pp 1415–1426

  109. Bajeh AO, Adeleke HO, Mojeed HA, Balogun AO, Abikoye OC, Usman-Hamza FE (2021) Ensemble models for predicting warts treatment methods. J Eng Sci Technol 16(2):1030–1052

    Google Scholar 

  110. Lara JVM, Velásquez RMA (2022) Low-cost image analysis with convolutional neural network for herpes zoster. Biomedical Signal Processing and Control 71:103250

    Article  Google Scholar 

  111. Guo K, Li T, Huang R, Kang J (2017) Deep Convolution Neural Network Discriminator for Distinguishing Seborrheic Keratosis and Flat Warts. In: Proceedings of the IEEE 15th International Conference on Dependable, Autonomic and Secure Computing, 15th International Conference on Pervasive Intelligence and Computing, 3rd International Conference on Big Data Intelligence and Computing and Cyber Science and Technology Congress, Lake Buena Vista, FL, USA, pp 16–21

  112. Back S, Lee S, Shin S, Yu Y, Yuk T, Jong S, Ryu S, Lee K (2021) Robust skin disease classification by distilling deep neural network ensemble for the mobile diagnosis of herpes zoster. IEEE Access 9:20156–20169

    Article  Google Scholar 

  113. Prodeep AR, Araf R, Ray P, Ulubbi MSA, Ananna SN, Mridha MF (2022) Acne and Rosacea Detection from Images using Deep CNN's EfficientNet. In: 2022 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI), pp 1–7

  114. Park S, Chien AL, Lin B, Li K (2023) Faces: a deep-learning-based parametric model to improve rosacea diagnoses. Appl Sci 13(2):970

    Article  Google Scholar 

  115. Saraswathi C, Pushpa, B (2023) Ensemble of pre-learned deep learning model and an optimized lstm for alopecia areata classification. Journal of Intelligent & Fuzzy Systems (Preprint), 1–12

  116. Saraswathi C, Pushpa B (2023) Ab-mtedeep classifier trained with aagan for the identification and classification of alopecia areata. Eng Technol Appl Sci Res 13(3):10895–10900

    Article  Google Scholar 

  117. Saraswathi C, Pushpa B (2023) Frcnn based deep learning for identification and classification of alopecia areata. In: 2023 Fifth International Conference on Elec- trical, Computer and Communication Technologies (ICECCT), 1–7 IEEE

  118. Madke J, Sondur M, Bhatlawande S (2023) Alopecia Pattern Detection in Males using Classical Machine Learning. In 2023 International Conference on Inventive Computation Technologies (ICICT) pp 282–286

  119. Islam MB, Junayed MS, Sadeghzadeh A, Anjum N, Ahsan A, Shah AS (2023) Acne vulgaris detection and classification: a dual integrated deep CNN model. Informatica 47(4)

  120. Bansal K, Saini ML, Bhardwaj K, Prajapati L (2023) Acne Skin Disease Detection Using Convolutional Neural Network Model. In 2023 3rd International Conference on Technological Advancements in Computational Sciences (ICTACS) pp 249–255

  121. Samonte MJC, Borja J, Delariarte LMA, Ebanen JAA (2023) AcneCheck: an acne severity grading in teledermatology through computer vision. In: 2023 13th International Conference on Software Technology and Engineering (ICSTE), pp 118–126

  122. Junayed MS, Islam MB, Jeny AA, Sadeghzadeh A, Biswas T, Shah AS (2021) Scarnet: development and validation of a novel deep cnn model for acne scar classification with a new dataset. IEEE Access 10:1245–1258

    Article  Google Scholar 

  123. Rahmad C, Asmara RA, Agstriningtyas AS (2021) Acne vulgaris and rosacea skin diseases image classification using gray level co-occurance matrix and convolutional neural network. In: 2021 International Conference on Electrical and Information Technology (IEIT), pp 126–132

  124. Wang J, Wang C, Wang Z, Hounye AH, Li Z, Kong M, Hou M, Zhang J, Qi M (2023) A novel automatic acne detection and severity quantifica- tion scheme using deep learning. Biomed Signal Process Control 84:104803

    Article  Google Scholar 

  125. Niknejad N, Bidese-Puhl R, Bao Y, Payn KG, Zheng J (2023) Phenotyping of architecture traits of loblolly pine trees using stereo machine vision and deep learning: Stem diameter, branch angle, and branch diameter. Comput Electron Agric 211:107999

    Article  Google Scholar 

  126. Choupanzadeh R, Zadehgol A (2023) A deep neural network modeling methodology for efficient EMC assessment of shielding enclosures using MECA-generated RCS training data. IEEE Transactions on Electromagnetic Compatibility

  127. Kieslich CA, Alimirzaei F, Song H, Do M, Hall P (2021) Data-driven prediction of antiviral peptides based on periodicities of amino acid properties. Comput Aided Chem Eng 50:2019–2024 Elsevier

  128. Di Biasi L, De Marco F, Auriemma Citarella A, Castrillόn-Santana M, Barra P, Tortora G (2023) Refactoring and performance analysis of the main cnn architectures: using false negative rate minimization to solve the clinical images melanoma detection problem. BMC bioinformatics 24(1):386

    Article  Google Scholar 

  129. Kränke T, Tripolt-Droschl K, Röd L, Hofmann-Wellenhof R, Koppitz M, Tripolt M (2023) New ai-algorithms on smartphones to detect skin cancer in a clinical setting—a validation study. Plos one 18(2):0280670

    Article  Google Scholar 

  130. Gadde S, Puttagunta MK, Dhanalakshmi G, El-Ebiary YAB (2023) Efficiency analysis of firefly optimization-enhanced gan-driven convolutional model for cost-effective melanoma classification. Int J Adv Comput Sci Appl 14(11). https://doi.org/10.14569/IJACSA.2023.0141175

  131. Su Q, Hamed HNA, Isa MA, Hao X, Dai X (2024) A gan-based data augmen- tation method for imbalanced multi-class skin lesion classification. IEEE Access.

  132. Xiao P, Zhang X, Pan W, Ou X, Bontozoglou C, Chirikhina E, Chen D (2020) The development of a skin image analysis tool by using machine learning algorithms. Cosmetics 7(3):67

    Article  Google Scholar 

  133. Gracey LE, Zan S, Gracz J, Miner JJ, Moreau JF, Sperber J, Jethwani K, Hale TM, Kvedar JC (2018) Use of user-centered design to create a smartphone application for patient-reported outcomes in atopic dermatitis. NPJ digital medicine 1(1):33

    Article  Google Scholar 

  134. Trettin B, Danbjørg DB, Andersen F, Feldman S, Agerskov H (2021) Devel- opment of an mhealth app for patients with psoriasis undergoing biological treatment: participatory design study. JMIR dermatology 4(1):26673

    Article  Google Scholar 

  135. Tao Q, Liu S, Zhang J, Jiang J, Jin Z, Huang Y, Liu X, Lin S, Zeng X, Li X, Tao G (2023) Clinical applications of smart wearable sensors. Iscience 26(9)

  136. Mittal R, Jeribi F, Martin RJ, Malik V, Menachery SJ, Singh J (2024) DermCDSM: clinical decision support model for dermatosis using systematic approaches of machine learning and deep learning. IEEE Access

  137. Dulmage B, Tegtmeyer K, Zhang MZ, Colavincenzo M, Xu S (2021) A point- of-care, real-time artificial intelligence system to support clinician diagnosis of a wide range of skin diseases. J Investig Dermatol 141(5):1230–1235

    Article  Google Scholar 

  138. Kaggle (2019) Dermnet. https://www.kaggle.com/datasets/shubhamgoel27/dermnet. Accessed: 19-Apr-2024

  139. Mendonça T, Ferreira PM, Marques JS, Marcal AR, Rozeira J (2013) PH 2-A dermoscopic image database for research and benchmarking. In: 2013 35th annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, pp 5437-5440

  140. Rotemberg V, Kurtansky N, Betz-Stablein B, Caffery L, Chousakos E, Codella N, Combalia M, Dusza S, Guitera P, Gutman D et al (2021) A patient- centric dataset of images and metadata for identifying melanomas using clinical context. Scientific data 8(1):34

    Article  Google Scholar 

  141. Tschandl P, Rosendahl C, Kittler H (2018) The ham10000 dataset, a large collec- tion of multi-source dermatoscopic images of common pigmented skin lesions. Scientific data 5(1):1–9

    Article  Google Scholar 

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  1. Pragya Gupta and Jagannath Nirmal contributed equally to this work.

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  1. Department of Electronics Engineering, K.J.Somaiya College of Engineering, Vidyavihar, Mumbai, 400077, Maharashtra, India

    Pragya Gupta, Jagannath Nirmal & Ninad Mehendale

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  2. Jagannath Nirmal
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Conceptualization was done by Pragya Gupta (PG), Jagannath Nirmal (JN), and Ninad Mehendale (NM). The review work was done by PG. All the summarization was performed by PG,JN, and NM. The manuscript draft was prepared by PG, and corrections were made by JN and NM. Data analysis and graphics designing were done by PG.

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Correspondence to Ninad Mehendale.

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Gupta, P., Nirmal, J. & Mehendale, N. A survey on computer vision approaches for automated classification of skin diseases. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-19301-w

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