Analysis and Prediction of Skin Cancer Disease using Ensemble CNN Technique

The research introduces a Weighted Ensemble CNN methodology to enhance skin cancer detection accuracy by leveraging multiple CNN models. Emphasizing the fusion of diverse model capabilities, this approach assigns higher weights to better-performing models, aiming to bolster accuracy and robustness. Experimentation on three distinct architectures using the HAM10000 dataset showcases the potential of ensemble learning in CNNs. While simple architectures combined for prediction yield promising results in less time, findings highlight Model 1’s potential for further improvement with extended training epochs. The study explores weighted ensemble outputs, revealing a significant accuracy boost through optimized weight configurations. Grid search methods identify an optimal ensemble ratio, achieving a 74.23% accuracy, surpassing individual model accuracies. While showing promise for diverse applications, including real-time medical diagnosis, further exploration and refinement are crucial. The paper outlines potential research paths, urging investigation into transfer learning’s efficacy, larger dataset evaluations, and broader medical diagnostic implementations. The conclusion underscores the need for continued research before confidently deploying these techniques in medical scenarios, emphasizing the potential for refinement and wider applicability in the realm of medical diagnosis.