Version 1
: Received: 3 November 2024 / Approved: 4 November 2024 / Online: 5 November 2024 (10:56:17 CET)
How to cite:
El Ouanas, B.; Mohamed Rafik Aymene, B.; Akram Abderraouf, G.; Ammar, C.; Ouamane, A. Deep Learning-Driven Skin Cancer Classification via CNN Features and Random Forest Classifier. Preprints2024, 2024110298. https://doi.org/10.20944/preprints202411.0298.v1
El Ouanas, B.; Mohamed Rafik Aymene, B.; Akram Abderraouf, G.; Ammar, C.; Ouamane, A. Deep Learning-Driven Skin Cancer Classification via CNN Features and Random Forest Classifier. Preprints 2024, 2024110298. https://doi.org/10.20944/preprints202411.0298.v1
El Ouanas, B.; Mohamed Rafik Aymene, B.; Akram Abderraouf, G.; Ammar, C.; Ouamane, A. Deep Learning-Driven Skin Cancer Classification via CNN Features and Random Forest Classifier. Preprints2024, 2024110298. https://doi.org/10.20944/preprints202411.0298.v1
APA Style
El Ouanas, B., Mohamed Rafik Aymene, B., Akram Abderraouf, G., Ammar, C., & Ouamane, A. (2024). Deep Learning-Driven Skin Cancer Classification via CNN Features and Random Forest Classifier. Preprints. https://doi.org/10.20944/preprints202411.0298.v1
Chicago/Turabian Style
El Ouanas, B., Chouchane Ammar and Abdelmalik Ouamane. 2024 "Deep Learning-Driven Skin Cancer Classification via CNN Features and Random Forest Classifier" Preprints. https://doi.org/10.20944/preprints202411.0298.v1
Abstract
Skin cancer is one of the most common and potentially deadly forms of cancer, making early and accurate detection crucial for effective treatment. In this context, leveraging advanced machine learning techniques can significantly enhance diagnostic accuracy and efficiency. This paper presents an intelligent system for monitoring skin cancer, leveraging a deep feature extraction approach based on Convolutional Neural Networks (CNNs) combined with a Random Forest (RF) classifier. The system processes skin cancer images to extract features using three pre-trained CNN models: ResNet18, Darknet19, and MobileNetv2, which are then used to train an RF classifier for accurate skin cancer detection. The proposed model is designed for edge deployment, making it suitable for real-time applications. Experiments were conducted using the "Skin Cancer: Malignant vs. Benign" dataset, specifically curated for skin cancer classification. The results demonstrate the effectiveness of the proposed system, achieving an accuracy of 82.85%, with a precision of 82.69%, a sensitivity of 82.86%, a specificity of 82.86%, and an F1-Score of 82.75%.
Keywords
Skin Cancer Classification; ISIC; Deep Learning; Machine Learning; CNN; RF
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
