Hybrid Model with Wavelet Decomposition and EfficientNet for Accurate Skin Cancer Classification

Aboulmira, Amina and Hrimech, Hamid and Lachgar, Mohamed and Hanine, Mohamed and Osorio García, Carlos Manuel and Méndez Mezquita, Gerardo and Ashraf, Imran UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, carlos.osorio@uneatlantico.es, UNSPECIFIED, UNSPECIFIED (2025) Hybrid Model with Wavelet Decomposition and EfficientNet for Accurate Skin Cancer Classification. Journal of Cancer, 16 (2). pp. 506-520. ISSN 1837-9664

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Abstract

Faced with anomalies in medical images, Deep learning is facing major challenges in detecting, diagnosing, and classifying the various pathologies that can be treated via medical imaging. The main challenges encountered are mainly due to the imbalance and variability of the data, as well as its complexity. The detection and classification of skin diseases is one such challenge that researchers are trying to overcome, as these anomalies present great variability in terms of appearance, texture, color, and localization, which sometimes makes them difficult to identify accurately and quickly, particularly by doctors, or by the various Deep Learning techniques on offer. In this study, an innovative and robust hybrid architecture is unveiled, underscoring the symbiotic potential of wavelet decomposition in conjunction with EfficientNet models. This approach integrates wavelet transformations with an EfficientNet backbone and incorporates advanced data augmentation, loss function, and optimization strategies. The model tested on the publicly accessible HAM10000 and ISIC2017 datasets has achieved an accuracy rate of 94.7%, and 92.2% respectively.

Item Type:	Article
Uncontrolled Keywords:	skin lesion, transfer learning, wavelet decomposition, image processing, convolutional neural networks
Subjects:	Subjects > Engineering
Divisions:	Europe University of Atlantic > Research > Scientific Production Ibero-american International University > Research > Scientific Production Universidad Internacional do Cuanza > Research > Scientific Production
Depositing User:	Sr Bibliotecario
Date Deposited:	22 Jan 2025 17:08
Last Modified:	22 Jan 2025 17:08
URI:	http://repositorio.funiber.org/id/eprint/16273

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