An Integrated Deep Learning Model for Skin Cancer Detection Using Hybrid Feature Fusion Technique

TL;DR

This paper introduces a hybrid deep learning framework combining InceptionV3 and DenseNet121 models with feature fusion for accurate skin cancer classification, achieving over 92% accuracy and outperforming existing methods.

Contribution

The study presents a novel hybrid deep learning approach with feature fusion that significantly improves skin cancer detection accuracy over prior models.

Findings

Achieved 92.27% detection accuracy

Demonstrated high sensitivity and specificity

Outperformed existing skin cancer classification models

Abstract

Skin cancer is a serious and potentially fatal disease caused by DNA damage. Early detection significantly increases survival rates, making accurate diagnosis crucial. In this groundbreaking study, we present a hybrid framework based on Deep Learning (DL) that achieves precise classification of benign and malignant skin lesions. Our approach begins with dataset preprocessing to enhance classification accuracy, followed by training two separate pre-trained DL models, InceptionV3 and DenseNet121. By fusing the results of each model using the weighted sum rule, our system achieves exceptional accuracy rates. Specifically, we achieve a 92.27% detection accuracy rate, 92.33% sensitivity, 92.22% specificity, 90.81% precision, and 91.57% F1-score, outperforming existing models and demonstrating the robustness and trustworthiness of our hybrid approach. Our study represents a significant advance in skin cancer diagnosis and provides a promising foundation for further research in the field. With the potential to save countless lives through earlier detection, our hybrid deep-learning approach is a game-changer in the fight against skin cancer.