Version 1
: Received: 3 November 2023 / Approved: 3 November 2023 / Online: 6 November 2023 (01:19:45 CET)
How to cite:
NATHA, P.; POTHURAJU, R. Skin Cancer Detection Using Machine Learning Classification Models. Preprints2023, 2023110248. https://doi.org/10.20944/preprints202311.0248.v1
NATHA, P.; POTHURAJU, R. Skin Cancer Detection Using Machine Learning Classification Models. Preprints 2023, 2023110248. https://doi.org/10.20944/preprints202311.0248.v1
NATHA, P.; POTHURAJU, R. Skin Cancer Detection Using Machine Learning Classification Models. Preprints2023, 2023110248. https://doi.org/10.20944/preprints202311.0248.v1
APA Style
NATHA, P., & POTHURAJU, R. (2023). Skin Cancer Detection Using Machine Learning Classification Models. Preprints. https://doi.org/10.20944/preprints202311.0248.v1
Chicago/Turabian Style
NATHA, P. and RAJARAJESWARI POTHURAJU. 2023 "Skin Cancer Detection Using Machine Learning Classification Models" Preprints. https://doi.org/10.20944/preprints202311.0248.v1
Abstract
In recent years, computer-aided analysis techniques have emerged as valuable tools in assisting dermatologists by providing objective and efficient analysis of skin cancer images. This paper utilizes the combination of the Contourlet Transform (CT) and Local Binary Pattern (LBP) techniques for accurately recognizing borders, contrast changes, and shapes of skin cancer images. These results often contain many features, leading to high computational costs and potential over-fitting issues. Hence, we applied Particle Swarm Optimization (PSO) to select the most informative and discriminating features, reducing the dimensionality while retaining important information for accurate classification. After reducing the feature set with PSO, we applied these sets to Machine learning classification algorithms: Support Vector Machine (SVM), Random Forest (RF), and Neural Networks (NN). The results show that SVM has the lowest time complexity of 0.0458 seconds, followed by the Neural Network at 0.08730 seconds, and the Random Forest model has the highest time complexity of 0.1622 seconds. The SVM and Neural Network models are faster to train than the Random Forest model, making them more suitable for real-time or latency-sensitive applications. We also compared our proposed model with the state-of-the-art models and obtained the accuracy of 86.9%, which is the highest among the models.
Keywords
Skin cancer; Contourlet Transform (CT); Particle Swarm Optimization (PSO); Support Vector Machine (SVM); Random Forest (RF); Neural Network (NN)
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.
