Skin-NeT: Skin Cancer Diagnosis Using VGG and ResNet-Based Ensemble Learning Approaches

To address the critical issue of late skin cancer diagnosis and its severe implications, this study leverages the latest in Computer Aided Diagnosis (CAD) and machine learning technologies.Despite the alignment of these technologies with professional medical diagnostics, challenges such as data imbalance, management of extensive datasets, and the need for high-quality images for superior feature extraction continue to pose significant hurdles.To overcome these challenges, this work introduced a novel approach utilizing ensemble learning, which significantly enhances the accuracy of early skin cancer detection.This research elaborates on the creation of two distinct ensemble models: one that combines the capabilities of VGG-16 and ResNet-50, and another that utilizes VGG-19 and Xception.These combinations were specifically chosen for their complementary strengths in deep learning and feature extraction, which are crucial for improving diagnostic accuracy.The models were trained on a comprehensive dataset of over 3000 skin images, achieving a groundbreaking training accuracy of 100% and a testing accuracy that reaches up to 85%.The rationale behind selecting these models for ensemble approach is their proven effectiveness in deep learning tasks.VGG models are renowned for their deep convolutional networks that excel in capturing intricate details, while ResNet models effectively address the vanishing gradient problem, enabling deeper network training without compromising performance.This strategic amalgamation enhances the ability to tackle the complexities of skin cancer detection.In comparative analysis, I eschew specific study references for a broader perspective on performance enhancement.The accuracy of these proposed models shows a substantial increase over existing methods, with testing accuracies advancing from the typical range of 75% to 84% observed in prior works, to as high as 85% in my models.This improvement not only demonstrates the superiority of ensemble learning approach over single-model methods but also establishes a new benchmark in the accuracy and reliability of skin cancer diagnostic tools.