Abstract
Melanoma is one of the most dangerous and deadly cancers in the world. In this contribution, we proposed a convolutional neural network architecture implemented in its design by using genetic algorithms. The aim is to find the best structure of a neural network to improve melanoma classification. An experimental study has evaluated the presented approach, conducted using a refined subset of images from ISIC, one of the most referenced datasets used for melanoma classification. The genetic algorithm implemented for the convolutional neural network design allows the population to evolve in subsequent generations to achieve fitness optimally. Convergence leads to the survival of a set of neural network populations representing the best individuals designated to optimize the network for melanoma classification. Our hybrid approach for the design of CNN for melanoma detection reaches 94% in accuracy, 90% in sensitivity, 97% in specificity, and 98% in precision. The preliminary results suggest that the proposed method could improve melanoma classification by eliminating the necessity for user interaction and avoiding a priori network architecture selection.
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Di Biasi, L., De Marco, F., Auriemma Citarella, A., Barra, P., Piotto Piotto, S., Tortora, G. (2023). Hybrid Approach for the Design of CNNs Using Genetic Algorithms for Melanoma Classification. In: Rousseau, JJ., Kapralos, B. (eds) Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges. ICPR 2022. Lecture Notes in Computer Science, vol 13643. Springer, Cham. https://doi.org/10.1007/978-3-031-37660-3_36
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DOI: https://doi.org/10.1007/978-3-031-37660-3_36
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