Optimized deep learning architecture for melanoma detection : leveraging ResNet50V2.5 in dermatological imaging
Subaida Beevi, Shafeena (Noorul Islam Centre for Higher Education (Índia))
R. S., Vinod Kumar (Noorul Islam Centre for Higher Education (Índia))
D., Shahi (Noorul Islam Centre for Higher Education (Índia))
S. S., Kumar (Noorul Islam Centre for Higher Education (Índia))

Date:	2026
Abstract:	Melanoma is a highly aggressive form of skin cancer that greatly impacts the global mortality rate related to skin cancer. Accurate identification and precise assessment of illness severity are essential for improving patient outcomes. The automatic classification of skin lesions by imaging is challenging due to the complex differences in their visual characteristics. This study employs deep learning algorithms to identify and distinguish between benign and malignant melanoma skin cancer. The malignancy is classified into seven distinct types: melanoma, melanocytic nevus, basal cell carcinoma, actinic keratosis, benign keratosis, dermatofibroma, and vascular lesion. The preliminary step of the proposed system entails a preprocessing stage where normalization and data augmentation techniques are utilized to prepare the HAM10000 dataset for the classification of benign and malignant cancer lesions. This research proposes a novel variant of the Residual Neural Network (ResNet), namely ResNet50V2. 5, for enhanced picture categorization, optimizing training efficiency by circumventing unnecessary layers and improving model performance. A comparative investigation of five designs, including ResNet101, ResNet101V2, ResNet50, ResNet50V2, and ResNet50V2. 5, demonstrates that the ResNet50V2. 5 model attains superior accuracy, achieving a classification performance of 99. 17%, thereby surpassing existing architectures in skin cancer diagnosis.
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Published in:	ELCVIA, Vol. 25, Num. 1 (2026) , p. 22-42 (Regular Issue) , ISSN 1577-5097

Adreça original: https://elcvia.cvc.uab.cat/article/view/2271
Adreça alternativa: https://raco.cat/index.php/ELCVIA/article/view/980000007323
DOI: 10.5565/rev/elcvia.2271

21 p, 2.8 MB

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