A model for skin cancer using combination of ensemble learning and deep learning

Hosseinzadeh, Mehdi; Hussain, Dildar; Zeki Mahmood, Firas Muhammad; Alenizi, Farhan A.; Varzeghani, Amirhossein Noroozi; Asghari, Parvaneh; Darwesh, Aso; Malik, Mazhar Hussain; Lee, Sang Woong

Abstract

Skin cancer has a significant impact on the lives of many individuals annually and is recognized as the most prevalent type of cancer. In the United States, an estimated annual incidence of approximately 3.5 million people receiving a diagnosis of skin cancer underscores its widespread prevalence. Furthermore, the prognosis for individuals afflicted with advancing stages of skin cancer experiences a substantial decline in survival rates. This paper is dedicated to aiding healthcare experts in distinguishing between benign and malignant skin cancer cases by employing a range of machine learning and deep learning techniques and different feature extractors and feature selectors to enhance the evaluation metrics. In this paper, different transfer learning models are employed as feature extractors, and to enhance the evaluation metrics, a feature selection layer is designed, which includes diverse techniques such as Univariate, Mutual Information, ANOVA, PCA, XGB, Lasso, Random Forest, and Variance. Among transfer models, DenseNet-201 was selected as the primary feature extractor to identify features from data. Subsequently, the Lasso method was applied for feature selection, utilizing diverse machine learning approaches such as MLP, XGB, RF, and NB. To optimize accuracy and precision, ensemble methods were employed to identify and enhance the best-performing models. The study provides accuracy and sensitivity rates of 87.72% and 92.15%, respectively.

• Journal Article Type: Article
• Acceptance Date: Mar 13, 2024
• Online Publication Date: May 31, 2024
• Publication Date: May 31, 2024
• Deposit Date: Jun 11, 2024
• Publicly Available Date: Jun 11, 2024
• Journal: PloS one
• Electronic ISSN: 1932-6203
• Publisher: Public Library of Science
• Peer Reviewed: Peer Reviewed
• Volume: 19
• Issue: 5
• Article Number: e0301275
• DOI: https://doi.org/10.1371/journal.pone.0301275
• Keywords: Deep Learning, Algorithms, Skin Neoplasms - pathology, Humans, Machine Learning
• Public URL: https://uwe-repository.worktribe.com/output/12040668
• Publisher URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0301275