TY  - JOUR
IS  - 2
JF  - Journal of Cancer
SP  - 506
UR  - http://doi.org/10.7150/jca.101574
N2  - 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.
VL  - 16
KW  - skin lesion
KW  -  transfer learning
KW  -  wavelet decomposition
KW  -  image processing
KW  -  convolutional neural networks
ID  - uninimx16273
EP  - 520
AV  - public
SN  - 1837-9664
TI  - Hybrid Model with Wavelet Decomposition and EfficientNet for Accurate Skin Cancer Classification
Y1  - 2025/01//
A1  - Aboulmira, Amina
A1  - Hrimech, Hamid
A1  - Lachgar, Mohamed
A1  - Hanine, Mohamed
A1  - Osorio García, Carlos Manuel
A1  - Méndez Mezquita, Gerardo
A1  - Ashraf, Imran
ER  -