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

arXiv: https://arxiv.org/abs/2410.14489 · HTML: https://mlsanity.com/paper/2410.14489/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 44 references that are in the map.
- First posted on arXiv 18 Oct 2024.

## Builds on (most cited first, 6 of 6)

- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction](https://mlsanity.com/paper/2401.12262/) (2024)
- [Machine Learning-based Lung and Colon Cancer Detection using Deep Feature Extraction and Ensemble Learning](https://mlsanity.com/paper/2206.01088/) (2022)
- [A Dependable Hybrid Machine Learning Model for Network Intrusion Detection](https://mlsanity.com/paper/2212.04546/) (2022)
- [New Interpretations of Normalization Methods in Deep Learning](https://mlsanity.com/paper/2006.09104/) (2020)
- [Empowering COVID-19 Detection: Optimizing Performance Through Fine-Tuned EfficientNet Deep Learning Architecture](https://mlsanity.com/paper/2311.16593/) (2023)

## Abstract (arXiv)

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.