Skin Lesion Classification Using a Soft Voting Ensemble of Convolutional Neural Networks

TL;DR

This paper introduces a skin cancer classification method using a soft voting ensemble of CNNs, achieving high accuracy on multiple datasets by combining segmentation and ensemble techniques for improved diagnostic performance.

Contribution

The study presents a novel ensemble approach with a hybrid segmentation method that enhances skin lesion classification accuracy and robustness across benchmark datasets.

Findings

Achieved over 90% accuracy on all datasets

Effective segmentation improved focus on clinically relevant features

Ensemble method balances accuracy and computational efficiency

Abstract

Skin cancer can be identified by dermoscopic examination and ocular inspection, but early detection significantly increases survival chances. Artificial intelligence (AI), using annotated skin images and Convolutional Neural Networks (CNNs), improves diagnostic accuracy. This paper presents an early skin cancer classification method using a soft voting ensemble of CNNs. In this investigation, three benchmark datasets, namely HAM10000, ISIC 2016, and ISIC 2019, were used. The process involved rebalancing, image augmentation, and filtering techniques, followed by a hybrid dual encoder for segmentation via transfer learning. Accurate segmentation focused classification models on clinically significant features, reducing background artifacts and improving accuracy. Classification was performed through an ensemble of MobileNetV2, VGG19, and InceptionV3, balancing accuracy and speed for real-world deployment. The method achieved lesion recognition accuracies of 96.32\%, 90.86\%, and 93.92\% for the three datasets. The system performance was evaluated using established skin lesion detection metrics, yielding impressive results.