An optimal graph convolutional vision neural network with explainable feature optimization for improved skin cancer detection.

ABSTRACT: Despite advancements in skin cancer diagnosis procedures, misclassification rates in early detection remain high, leading to delayed treatments and reduced survival rates. Existing manual diagnostic methods are often prone to inter-observer variability and human error, while traditional machine learning models struggle with imbalanced datasets and insufficient feature generalization. To address these challenges, this work proposes an Optimal Skin Cancer Classification Network (OSCC-Net), developed on the International Skin Imaging Collaboration-2019 (ISIC-2019) dataset. The model integrates an Adaptive Minority Over-Sampling Procedure (AMOP) to balance under-represented lesion classes, ensuring robust learning for minority lesion classes. The Stochastic Neighbourhood T-Distilling driven Sc

SUBMITTER: Pandala ML 

PROVIDER: S-EPMC12797544 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature