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© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Efficient skin cancer detection using images is a challenging task in the healthcare domain. In today’s medical practices, skin cancer detection is a time-consuming procedure that may lead to a patient’s death in later stages. The diagnosis of skin cancer at an earlier stage is crucial for the success rate of complete cure. The efficient detection of skin cancer is a challenging task. Therefore, the numbers of skilful dermatologists around the globe are not enough to deal with today’s healthcare. The huge difference between data from various healthcare sector classes leads to data imbalance problems. Due to data imbalance issues, deep learning models are often trained on one class more than others. This study proposes a novel deep learning-based skin cancer detector using an imbalanced dataset. Data augmentation was used to balance various skin cancer classes to overcome the data imbalance. The Skin Cancer MNIST: HAM10000 dataset was employed, which consists of seven classes of skin lesions. Deep learning models are widely used in disease diagnosis through images. Deep learning-based models (AlexNet, InceptionV3, and RegNetY-320) were employed to classify skin cancer. The proposed framework was also tuned with various combinations of hyperparameters. The results show that RegNetY-320 outperformed InceptionV3 and AlexNet in terms of the accuracy, F1-score, and receiver operating characteristic (ROC) curve both on the imbalanced and balanced datasets. The performance of the proposed framework was better than that of conventional methods. The accuracy, F1-score, and ROC curve value obtained with the proposed framework were 91%, 88.1%, and 0.95, which were significantly better than those of the state-of-the-art method, which achieved 85%, 69.3%, and 0.90, respectively. Our proposed framework may assist in disease identification, which could save lives, reduce unnecessary biopsies, and reduce costs for patients, dermatologists, and healthcare professionals.

Details

Title
An Efficient Deep Learning-Based Skin Cancer Classifier for an Imbalanced Dataset
Author
Talha Mahboob Alam 1   VIAFID ORCID Logo  ; Shaukat, Kamran 2   VIAFID ORCID Logo  ; Waseem Ahmad Khan 3   VIAFID ORCID Logo  ; Hameed, Ibrahim A 4   VIAFID ORCID Logo  ; Latifah Abd Almuqren 5 ; Muhammad Ahsan Raza 3 ; Aslam, Memoona 3 ; Luo, Suhuai 6 

 Department of Computer Science and Information Technology, Virtual University of Pakistan, Lahore 54000, Pakistan 
 School of Information and Physical Sciences, The University of Newcastle, Newcastle, NSW 2308, Australia; Department of Data Science, University of the Punjab, Lahore 54890, Pakistan 
 School of Computer Science, National College of Business Administration & Economics, Lahore 54660, Pakistan 
 Department of ICT and Natural Sciences, Norwegian University of Science and Technology, 7034 Trondheim, Norway 
 IS Department, College of Computer and Information Science, Princess Nourah Bint Abdulrahman University (PNU), P.O. Box 84428, Riyadh 11671, Saudi Arabia 
 School of Information and Physical Sciences, The University of Newcastle, Newcastle, NSW 2308, Australia 
First page
2115
Publication year
2022
Publication date
2022
Publisher
MDPI AG
e-ISSN
20754418
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2716521055
Copyright
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.