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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

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


Mehdi Hosseinzadeh

Dildar Hussain

Firas Muhammad Zeki Mahmood

Farhan A. Alenizi

Amirhossein Noroozi Varzeghani

Parvaneh Asghari

Aso Darwesh

Sang Woong Lee


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
Keywords Deep Learning, Algorithms, Skin Neoplasms - pathology, Humans, Machine Learning
Public URL
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