Abstract
Features of skin cancer have a certain impact on computer-aided diagnosis (CAD) systems. Researchers had used different techniques to experience with patterns. The melanoma lesion could also be identified with a different texture, shape, and clinical features. The proposed study has used 22 features of texture, 12 features of shape. The study has exposed three feature selection (FS) techniques like gradient boosting (GB), particle swarm optimization (PSO), and statistical approach. The features are evaluated with these methods and highlighted the effectiveness of each feature for the classification of melanoma. Selected key features have less than the cost of computation. The reduced feature set can make classification better than per the selection of the model. The random forest has the highest performance based on accuracy as it got the highest accuracy of 97.1% on GB feature sets. Decision tree and K-nearest neighbors have shown a decent accuracy of 96.8 and 93.3% on GB feature sets. The study rewards upcoming explorations to select an effective subset of features for machine learning and deep learning techniques.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Benco, M., Kamencay, P., Radilova, M., Hudec, R., Sinko, M.: The comparison of color texture features extraction based on 1D GLCM with deep learning methods. In: 2020 International Conference on Systems, Signals and Image Processing (IWSSIP). pp. 285–289 (2020)
Tiwari, P., Prasanna, P., Rogers, L., Wolansky, L., Badve, C., Sloan, A., Cohen, M., Madabhushi, A.: Texture descriptors to distinguish radiation necrosis from recurrent brain tumors on multi-parametric MRI. In: Medical Imaging 2014: Computer-Aided Diagnosis, vol. 9035, p. 90352 (2014)
Filali, Y., Sabri, M.A., Aarab, A.: Improving Skin Cancer Classification Based on Features Fusion and Selection (2020)
Afza, F., Khan, M.A., Sharif, M., Rehman, A.: Microscopic skin laceration segmentation and classification: a framework of statistical normal distribution and optimal feature selection. Microsc. Res. Tech. 82(9), 1471–1488 (2019)
Tapia, D.I., Corchado, J.M.: An ambient intelligence based multi-agent system for alzheimer health care. Int. J. Ambient. Comput. Intell. (IJACI) 1(1), 15–26 (2009)
Gaonkar, R., Singh, K., Prashanth, G.R., Kuppili, V.: Lesion analysis towards melanoma detection using soft computing techniques. Clin. Epidemiol. Glob. Health 8(2), 501–508 (2020)
Rehman, A., Khan, M.A., Mehmood, Z., Saba, T., Sardaraz, M., Rashid, M.: Microscopic melanoma detection and classification: a framework of pixel-based fusion and multilevel features reduction. Microsc. Res. Tech. 83(4), 410–423 (2020)
Matallah, H., Belalem, G., Bouamrane, K.: Towards a new model of storage and access to data in big data and cloud computing. Int. J. Ambient. Comput. Intell. (IJACI) 8(4), 31–44 (2017)
Dey, N., Ashour, A.S.: Ambient Intelligence in healthcare: a state-of-the-art. Glob. J. Comput. Sci. Technol. (2017)
Damian, F.A., Moldovanu, S., Dey, N., Ashour, A.S., Moraru, L.: Feature selection of non-dermoscopic skin lesion images for nevus and melanoma classification. Computation 8(2), 41 (2020)
Reshma, M., Shan, B.P.: A clinical decision support system for micro panoramic melanoma detection and grading using soft computing technique. Measurement 163, 108024 (2020)
Ain, Q.U., Al-Sahaf, H., Xue, B., Zhang, M.: Generating knowledge-guided discriminative features using genetic programming for melanoma detection. In: IEEE Transactions on Emerging Topics in Computational Intelligence (2020)
Akram, T., Lodhi, H.M.J., Naqvi, S.R., Naeem, S., Alhaisoni, M., Ali, M., Haider, S.A., Qadri, N.N.: A multilevel features selection framework for skin lesion classification. Human-centric Comput. Inform. Sci. 10, 1–26 (2020)
Adjed, F., Gardezi, S.J.S., Ababsa, F., Faye, I., Dass, S.C.: Fusion of structural and textural features for melanoma recognition. IET Comput. Vis. 12(2), 185–195 (2018). https://dx.doi.org/10.1049/iet-cvi.2017.0193
Hagerty, J.R., Stanley, R.J., Almubarak, H.A., Lama, N., Kasmi, R., Guo, P., Drugge, R.J., Rabinovitz, H.S., Oliviero, M., Stoecker, W.V.: Deep learning and handcrafted method fusion: higher diagnostic accuracy for melanoma dermoscopy images. IEEE J. Biomed. Health Inform. 23(4), 1385–1391 (2019)
Li, X., Wu, J., Jiang, H., Chen, E.Z., Dong, X., Rong, R.: Skin lesion classification via combining deep learning features and clinical criteria representations. bioRxiv, 382010 (2018)
Abbas, Q., Celebi, M.E.: DermoDeep—a classification of melanoma-nevus skin lesions using multi-feature fusion of visual features and deep neural network. Multimed. Tools Appl. 78(16), 23559–23580 (2019)
Tan, T.Y., Zhang, L., Lim, C.P.: Adaptive melanoma diagnosis using evolving clustering, ensemble and deep neural networks. Knowl. Based Syst. 187, 104807 (2020)
Filali, Y., Khoukhi, H.E., Sabri, M.A., Aarab, A.: Efficient fusion of handcrafted and pretrained CNNs features to classify melanoma skin cancer. Multimed. Tools Appl. 1–20 (2020)
Kassem, M.A., Hosny, K.M., Fouad, M.M.: Skin lesions classification into eight classes for ISIC 2019 using deep convolutional neural network and transfer learning. IEEE Access 8, 114822–114832 (2020)
Belarbi, M.A., Mahmoudi, S.A., Mahmoudi, S., Belalem, G.: A new parallel and distributed approach for large scale images retrieval. In: International Conference of Cloud Computing Technologies and Applications. pp. 185–201. Springer, Cham (2017)
Khamparia, A., Singh, P.K., Rani, P., Samanta, D., Khanna, A., Bhushan, B.: An internet of health things-driven deep learning framework for detection and classification of skin cancer using transfer learning. Trans. Emerg. Telecommun. Technol. e3963 (2020)
Satheesha, T.Y.: Classification of Skin Lesion Using (Segmentation) Shape Feature Detection (2020)
Sheridan, R.P., Wang, M., Liaw, A., Ma, J., Gifford, E.: Correction to extreme gradient boosting as a method for quantitative structure—activity relationships. J. Chem. Inf. Model. 60(3), 1910–1910 (2020)
Rostami, M., Forouzandeh, S., Berahmand, K., Soltani, M.: Integration of multi-objective PSO based feature selection and node centrality for medical datasets. Genomics (2020)
Tao, G., Liu, Z., Cao, J., Liang, S.: Local difference ternary sequences descriptor based on unsupervised min redundancy mutual information feature selection. Multidimens. Syst. Signal Process. 31(3), 771–791 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Maiti, A., Chatterjee, B. (2021). The Effect of Different Feature Selection Methods for Classification of Melanoma. In: Bhattacharyya, S., Mršić, L., Brkljačić, M., Kureethara, J.V., Koeppen, M. (eds) Recent Trends in Signal and Image Processing. ISSIP 2020. Advances in Intelligent Systems and Computing, vol 1333. Springer, Singapore. https://doi.org/10.1007/978-981-33-6966-5_13
Download citation
DOI: https://doi.org/10.1007/978-981-33-6966-5_13
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-33-6965-8
Online ISBN: 978-981-33-6966-5
eBook Packages: EngineeringEngineering (R0)