Abstract
The healthcare industry has witnessed the emergence of deep learning (DL) along with blockchain technologies as potent instruments with considerable potential for transforming the sector, in the domain of imaging analysis of information. The healthcare industry is highly significant to society as it is responsible for preserving and enhancing human health. It includes preventive medicine, diagnostics, rehabilitation, therapy, and palliative care offerings. In recent years, there have been notable advancements and improvements in the medical field regarding using images by implementing DL and blockchain applications. Integrating DL and blockchain technology in healthcare imaging has presented novel prospects for cutting-edge research and advancement. Scholars are investigating using DL models to examine voluminous imaging datasets, thereby revealing valuable insights and patterns that can facilitate progress in healthcare knowledge and treatment methodologies. The adoption of blockchain technology in clinical trials contributes to promoting transparency and consistency. This is owing to the inherent advantages of blockchain, which enable the creation of a transparent and visible system. As a result, the integrity of data collected for research is being protected and trust in the outcomes of clinical trials is being fostered. Integrating DL and the blockchain system presents an intriguing chance to transform the field of telemedicine by facilitating the safe and confidential transfer and retention of medical images, thereby enabling remote diagnosis and advice. This chapter aims to showcase the applications of DL along with the blockchain in healthcare using an imaging dataset. Using a colossal collection of data, combined with deep learning and blockchain techniques, it can be trained to exhibit the desired behaviour. Applications of DL and blockchain technology with imaging dataset for various diseases such as cancer, diabetic retinopathy, Alzheimer's etc. can aid medical professionals in the early investigation and classification of diseases so that stricken are given effective therapies.
References
Singh B, Tatiya M, Shrivastava A, Verma D, Pratap Srivastava A, Rana A (2022) Detection of Alzheimer’s disease using deep learning, blockchain, and IoT cognitive data. In: 2022 2nd international conference on technological advancements in computational sciences (ICTACS), Oct 2022, pp 863–869.https://doi.org/10.1109/ICTACS56270.2022.9988058
Lyman GH, Moses HL (2016) Biomarker tests for molecularly targeted therapies—the key to unlocking precision medicine. N Engl J Med 375(1):4–6. https://doi.org/10.1056/NEJMp1604033
Razzak MI, Naz S, Zaib A (2018) Deep Learning for medical image processing: overview, challenges and the future. In: Lecture notes in computational vision and biomechanics, vol 26, pp 323–350
Miotto R, Wang F, Wang S, Jiang X, Dudley JT (2017) Deep learning for healthcare: review, opportunities and challenges. Brief Bioinform 19(6):1236–1246. https://doi.org/10.1093/bib/bbx044
Esteva A et al (2019) A guide to deep learning in healthcare. Nat Med 25(1):24–29. https://doi.org/10.1038/s41591-018-0316-z
Panch T, Szolovits P, Atun R (2018) Artificial intelligence, machine learning and health systems. J Glob Health 8(2):1–8. https://doi.org/10.7189/jogh.08.020303
Pilozzi A, Huang X (2020) Overcoming Alzheimer’s disease stigma by leveraging artificial intelligence and blockchain technologies. Brain Sci 10(3):183. https://doi.org/10.3390/brainsci10030183
Bergstra J, Bengio Y (2012) Random search for hyper-parameter optimization. J Mach Learn Res 13:281–305
Chang CC, Lin CJ (2011) LIBSVM: A Library for support vector machines. ACM Trans Intell Syst Technol 2(3). https://doi.org/10.1145/1961189.1961199
Sethi M, Ahuja S, Rani S, Bawa P, Zaguia A (2021) Classification of Alzheimer’s disease using gaussian-based Bayesian parameter optimization for deep convolutional LSTM network. Comput Math Methods Med 2021. https://doi.org/10.1155/2021/4186666
Yiğit A, Işik Z (2020) Applying deep learning models to structural MRI for stage prediction of Alzheimer’s disease. Turkish J Electr Eng Comput Sci 28(1):196–210. https://doi.org/10.3906/elk-1904-172
Brückner G, Hausen D, Härtig W, Drlicek M, Arendt T, Brauer K (1999) Cortical areas abundant in extracellular matrix chondroitin sulphate proteoglycans are less affected by cytoskeletal changes in Alzheimer’s disease. Neuroscience 92(3):791–805. https://doi.org/10.1016/S0306-4522(99)00071-8
Wang SH, Phillips P, Sui Y, Liu B, Yang M, Cheng H (2018) Classification of Alzheimer’s disease based on eight-layer convolutional neural network with leaky rectified linear unit and max pooling. J Med Syst 42(5):85. https://doi.org/10.1007/s10916-018-0932-7
Azari NP et al (1993) Early detection of Alzheimer’s disease: a statistical approach using positron emission tomographic data. J Cereb Blood Flow Metab 13(3):438–447. https://doi.org/10.1038/jcbfm.1993.58
Razavi F, Tarokh MJ, Alborzi M (2019) An intelligent Alzheimer’s disease diagnosis method using unsupervised feature learning. J Big Data 6(1). https://doi.org/10.1186/s40537-019-0190-7
Lecun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444. https://doi.org/10.1038/nature14539
Lee G et al (2019) Predicting Alzheimer’s disease progression using multi-modal deep learning approach. Sci Rep 9(1):1–12. https://doi.org/10.1038/s41598-018-37769-z
Venugopalan J, Tong L, Hassanzadeh HR, Wang MD (2021) Multimodal deep learning models for early detection of Alzheimer’s disease stage. Sci Rep 11(1):1–13. https://doi.org/10.1038/s41598-020-74399-w
Liu S, Liu S, Cai W, Pujol S, Kikinis R, Feng D (2014) Early diagnosis of Alzheimer’s disease with deep learning. In: 2014 IEEE 11th int. symp. biomed. imaging, ISBI 2014, pp 1015–1018. https://doi.org/10.1109/isbi.2014.6868045
Ortiz A, Munilla J, Górriz JM, Ramírez J (2016) Ensembles of deep learning architectures for the early diagnosis of the Alzheimer’s disease. Int J Neural Syst 26(7):1–23. https://doi.org/10.1142/S0129065716500258
Sarraf S, Tofighi G (2017) Deep learning-based pipeline to recognize Alzheimer’s disease using fMRI data. In: FTC 2016 - proc. futur. technol. conf., no December, pp 816–820. https://doi.org/10.1109/FTC.2016.7821697.
Pan D, Huang Y, Zeng A, Jia L, Song X (2019) Early Diagnosis of Alzheimer’s disease based on deep learning and GWAS. Commun Comput Inf Sci 1072(1):52–68. https://doi.org/10.1007/978-981-15-1398-5_4
Kazemi Y, Houghten S (2018) A deep learning pipeline to classify different stages of Alzheimer’s disease from fMRI data. In: 2018 IEEE conf. comput. intell. bioinforma. comput. biol. CIBCB 2018, no Mci, pp 1–8. https://doi.org/10.1109/CIBCB.2018.8404980
Feng C et al (2019) Deep learning framework for Alzheimer’s disease diagnosis via 3D-CNN and FSBi-LSTM. IEEE Access 7:63605–63618. https://doi.org/10.1109/ACCESS.2019.2913847
Qiu S et al (2020) Development and validation of an interpretable deep learning framework for Alzheimer’s disease classification. Brain 143(6):1920–1933. https://doi.org/10.1093/brain/awaa137
Ahila A, Poongodi M, Hamdi M, Bourouis S, Rastislav K, Mohmed F (2022) Evaluation of neuro images for the diagnosis of Alzheimer’s disease using deep learning neural network. Front Public Heal 10, no February, pp 834032. https://doi.org/10.3389/fpubh.2022.834032
Savaş S (2022) Detecting the Stages of Alzheimer’s disease with pre-trained deep learning architectures. Arab J Sci Eng 47(2):2201–2218. https://doi.org/10.1007/s13369-021-06131-3
Koga S, Ikeda A, Dickson DW (2022) Deep learning-based model for diagnosing Alzheimer’s disease and tauopathies. Neuropathol Appl Neurobiol 48(1):1–12. https://doi.org/10.1111/nan.12759
EL-Geneedy M, Moustafa HED, Khalifa F, Khater H, AbdElhalim E (2023) An MRI-based deep learning approach for accurate detection of Alzheimer’s disease. Alexandria Eng J 63:211–221. https://doi.org/10.1016/j.aej.2022.07.062
Kumar R et al (2021) An Integration of blockchain and AI for secure data sharing and detection of CT images for the hospitals. Comput Med Imaging Graph 87:101812. https://doi.org/10.1016/j.compmedimag.2020.101812
Munir K, Elahi H, Ayub A, Frezza F, Rizzi A (2019) Cancer diagnosis using deep learning: a bibliographic review. Cancers (Basel) 11(9):1–36. https://doi.org/10.3390/cancers11091235
Kaushal C, Singla A (2020) Automated segmentation technique with self-driven post-processing for histopathological breast cancer images. CAAI Trans Intell Technol 5(4):294–300. https://doi.org/10.1049/trit.2019.0077
Zhu W, Xie L, Han J, Guo X (2020) The application of deep learning in cancer prognosis prediction. Cancers (Basel) 12(3):1–19. https://doi.org/10.3390/cancers12030603
Kaushal C, Kaushal K, Singla A (2021) Firefly optimization-based segmentation technique to analyse medical images of breast cancer. Int J Comput Math 98(7):1293–1308. https://doi.org/10.1080/00207160.2020.1817411
Society RS (2013) Asymptotically efficient rank invariant test procedures author (s): Richard Peto and Julian Peto Reviewed work (s): Source : Journal of the Royal Statistical Society . Series A (General), 135(2) (1972), pp Published by : Wiley for th, vol 135, no 2, pp 185–207
Ahmed FE, Vos PW, Holbert D (2007) Modeling survival in colon cancer: a methodological review. Mol Cancer 6:1–12. https://doi.org/10.1186/1476-4598-6-15
Kaplan EL, Meier P (1958) Nonparametric estimation from incomplete observations. J Am Stat Assoc, vol 53, no March 2013, pp 457–81
Goossens N, Nakagawa S, Sun X, Hoshida Y (2015) Cancer biomarker discovery and validation. Transl Cancer Res 4(3):256–269. https://doi.org/10.3978/j.issn.2218-676X.2015.06.04
Tan M et al (2014) Lysine glutarylation is a protein posttranslational modification regulated by SIRT5. Cell Metab 19(4):605–617. https://doi.org/10.1016/j.cmet.2014.03.014
Jiang Y, Chen L, Zhang H, Xiao X (2019) Breast cancer histopathological image classification using convolutional neural networks with small SE-ResNet module. PLoS One 14(3). https://doi.org/10.1371/journal.pone.0214587
Han Z, Wei B, Zheng Y, Yin Y, Li K, Li S (2017) Breast cancer multi-classification from histopathological images with structured deep learning model. Sci Rep 7(1):1–10. https://doi.org/10.1038/s41598-017-04075-z
Kumar ES, Bindu CS, Madhu S (2020) Deep convolutional neural network-based analysis for breast cancer histology images, vol 1. Springer International Publishing
Jiang Y, Chen L, Zhang H, Xiao X (2019) Breast cancer histopathological image classification using convolutional neural networks with small SE-ResNet module. PLoS ONE 14(3):1–21. https://doi.org/10.1371/journal.pone.0214587
Hameed Z, Zahia S, Garcia-Zapirain B, Aguirre JJ, Vanegas AM (2020) Breast cancer histopathology image classification using an ensemble of deep learning models. Sensors (Switzerland) 20(16):1–17. https://doi.org/10.3390/s20164373
Yan R et al (2020) Breast cancer histopathological image classification using a hybrid deep neural network. Methods 173(2019):52–60. https://doi.org/10.1016/j.ymeth.2019.06.014
Gheshlaghi SH, Nok Enoch Kan C, Ye DH (2021) Breast cancer histopathological image classification with adversarial image synthesis. In: 2021 43rd annual international conference of the IEEE engineering in medicine & biology society (EMBC), pp 3387–3390. https://doi.org/10.1109/EMBC46164.2021.9630678
Zou Y, Zhang J, Huang S, Liu B (2022) Breast cancer histopathological image classification using attention high-order deep network. Int J Imaging Syst Technol 32(1):266–279. https://doi.org/10.1002/ima.22628
Ukwuoma CC, Hossain MA, Jackson JK, Nneji GU, Monday HN, Qin Z (2022) Multi-Classification of breast cancer lesions in histopathological images using DEEP_Pachi: multiple self-attention head. Diagnostics 12(5). https://doi.org/10.3390/diagnostics12051152
Ahmad N, Asghar S, Gillani SA (2022) Transfer learning-assisted multi-resolution breast cancer histopathological images classification. Vis Comput 38(8):2751–2770. https://doi.org/10.1007/s00371-021-02153-y
Obayya M, et al (2023) Hyperparameter optimizer with deep learning-based decision-support systems for histopathological breast cancer diagnosis. Cancers (Basel) 15(3). https://doi.org/10.3390/cancers15030885
Alyoubi WL, Shalash WM, Abulkhair MF (2020) Diabetic retinopathy detection through deep learning techniques: a review. Informatics Med Unlocked 20:100377. https://doi.org/10.1016/j.imu.2020.100377
Raja Memon DW, Lal DB, Aziz Sahto DA (2017) Diabetic retinopathy; frequency at level of hba1c greater than 6.5%. Prof Med J 24(2):234–238. https://doi.org/10.17957/tpmj/17.3616
Wu L (2013) Classification of diabetic retinopathy and diabetic macular edema. World J Diabetes 4(6):290. https://doi.org/10.4239/wjd.v4.i6.290
Jan S, Ahmad I, Karim S, Hussain Z, Rehman M, Shah MA (2018) Status of diabetic retinopathy and its presentation patterns in diabetics at ophthalomogy clinics. J Postgrad Med Inst 32(1):24–27
Seoud L, Hurtut T, Chelbi J, Cheriet F, Langlois JMP (2016) Red lesion detection using dynamic shape features for diabetic retinopathy screening. IEEE Trans Med Imaging 35(4):1116–1126. https://doi.org/10.1109/TMI.2015.2509785
Quellec G, Charrière K, Boudi Y, Cochener B, Lamard M (2017) Deep image mining for diabetic retinopathy screening. Med Image Anal 39:178–193. https://doi.org/10.1016/j.media.2017.04.012
Pratt H, Coenen F, Broadbent DM, Harding SP, Zheng Y (2016) Convolutional neural networks for diabetic retinopathy. Procedia Comput Sci 90(July):200–205. https://doi.org/10.1016/j.procs.2016.07.014
Ardiyanto I, Nugroho HA, Buana RLB (2017) Deep learning-based Diabetic Retinopathy assessment on embedded system. In: Proc. annu. int. conf. IEEE eng. med. biol. soc. EMBS, pp 1760–1763. https://doi.org/10.1109/EMBC.2017.8037184
Gao Z, Li J, Guo J, Chen Y, Yi Z, Zhong J (2019) Diagnosis of diabetic retinopathy using deep neural networks. IEEE Access 7(c):3360–3370. https://doi.org/10.1109/ACCESS.2018.2888639
Jiang H, Yang K, Gao M, Zhang D, Ma H, Qian W (2019) An interpretable ensemble deep learning model for diabetic retinopathy disease classification. In: Proc. annu. int. conf. IEEE eng. med. biol. soc. EMBS, pp 2045–2048.https://doi.org/10.1109/EMBC.2019.8857160
Qummar S et al (2019) A deep learning ensemble approach for diabetic retinopathy detection. IEEE Access 7:150530–150539. https://doi.org/10.1109/ACCESS.2019.2947484
Gangwar AK, Ravi V (2021) Diabetic retinopathy detection using transfer learning and deep learning, vol 1176. Springer, Singapore
Nguyen QH, et al (2020) Diabetic retinopathy detection using deep learning. In: ACM int. conf. proceeding ser., pp 103–107. https://doi.org/10.1145/3380688.3380709
Khan Z et al (2021) Diabetic retinopathy detection using VGG-NIN a deep learning architecture. IEEE Access 9:61408–61416. https://doi.org/10.1109/ACCESS.2021.3074422
Sebti R, Zroug S, Kahloul L, Benharzallah S (2022) A Deep Learning Approach for the Diabetic Retinopathy Detection. In: 2022 2nd international conference on intelligent technologies, CONIT 2022, no April, 2022, pp 459–469
Bilal A, Zhu L, Deng A, Lu H, Wu N (2022) AI-based automatic detection and classification of diabetic retinopathy using u-net and deep learning. Symmetry (Basel) 14(7). https://doi.org/10.3390/sym14071427
Chandrasekaran R, Loganathan B (2022) Retinopathy grading with deep learning and wavelet hyper-analytic activations. Vis Comput. https://doi.org/10.1007/s00371-022-02489-z
Mondal SS, Mandal N, Singh KK, Singh A, Izonin I (2023) EDLDR: an ensemble deep learning technique for detection and classification of diabetic retinopathy. Diagnostics 13(1):1–14. https://doi.org/10.3390/diagnostics13010124
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Sethi, M., Arora, J., Baggan, V., Verma, J., Snehi, M. (2024). Deep Learning and Blockchain Applications in Healthcare Sector Using Imaging Data. In: Kaushik, K., Sharma, I. (eds) Next-Generation Cybersecurity. Blockchain Technologies. Springer, Singapore. https://doi.org/10.1007/978-981-97-1249-6_7
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