Skin cancer classification leveraging multi-directional compact convolutional neural network ensembles and gabor wavelets.

ABSTRACT: Skin cancer (SC) is an important medical condition that necessitates prompt identification to ensure timely treatment. Although visual evaluation by dermatologists is considered the most reliable method, its efficacy is subjective and laborious. Deep learning-based computer-aided diagnostic (CAD) platforms have become valuable tools for supporting dermatologists. Nevertheless, current CAD tools frequently depend on Convolutional Neural Networks (CNNs) with huge amounts of deep layers and hyperparameters, single CNN model methodologies, large feature space, and exclusively utilise spatial image information, which restricts their effectiveness. This study presents SCaLiNG, an innovative CAD tool specifically developed to address and surpass these constraints. SCaLiNG leverages a collection o

SUBMITTER: Attallah O 

PROVIDER: S-EPMC11375051 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature