Abstract
Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. The aim of this work is to demonstrate two critical binary classification of skin lesions using Deep Learning algorithms. The proposed method based on a custom convolutional Neural Network trained end-to-end from images directly, using only pixels and disease labels as inputs, we train a CNN using a dataset of 3600 images. A deep learning based method convolutional neural network classifier is used for the stratification of the extracted features. After evaluation of model we find that the accuracy scores up to 72.73%, it achieved 76.15% for sensitivity, 83.06% for specificity, 77.40% for precision and 76.76% for F1 score.
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Oumoulylte, M., El Allaoui, A., Farhaoui, Y., Amounas, F., Qaraai, Y. (2023). Deep Learning Algorithms for Skin Cancer Classification. In: Farhaoui, Y., Rocha, A., Brahmia, Z., Bhushab, B. (eds) Artificial Intelligence and Smart Environment. ICAISE 2022. Lecture Notes in Networks and Systems, vol 635. Springer, Cham. https://doi.org/10.1007/978-3-031-26254-8_49
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DOI: https://doi.org/10.1007/978-3-031-26254-8_49
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