Simulation-Based Evaluation of Dense Convolutional Neural Networks for Skin Cancer Detection

Authors

Kavita Behara
Mangosuthu University of Technology image/svg+xml
Ernest Bhero
University of KwaZulu-Natal, 4041, South Africa
John Terhile Agee
University of KwaZulu-Natal, 4041, South Africa
DOI: https://doi.org/10.52549/ijeei.v13i1.5825
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Published: 24-02-2025 • Issue: Vol. 13 No. 1: March 2025 • Section: Regular paper

Abstract

Skin cancer, particularly melanoma, poses significant challenges to public health, with early detection being critical for effective treatment. Traditional diagnostic methods often fall short, particularly in resource-limited settings. In response, artificial intelligence (AI) techniques, especially deep learning models, have emerged as promising tools for automated skin cancer detection. This study evaluates the performance of Dense Convolutional Neural Networks (DCNNs) in classifying and detecting skin lesions, leveraging simulation-based approaches to assess the effectiveness of various AI models. Utilizing datasets such as HAM10000 and ISIC2017, which contain a wide variety of skin types and lesion stages, the models were trained and tested using key performance metrics such as accuracy, precision, recall, and F1-score. The results shows that DCNNs outperformed traditional machine learning techniques like Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Decision Trees (DT), demonstrating superior accuracy, generalization ability, and efficiency in handling large, imbalanced datasets. The simulation-based approach provided insights into the ability of DCNN models to manage dataset inconsistencies and class imbalances, showcasing their potential as robust tools for skin cancer detection. These findings highlight the ability of AI in advancing dermatological diagnostics, offering more timely and accurate detection, and potentially improving patient outcomes
Keywords: Deep learning; Machine learning; Medical diagnostics; Augmentation; Segmentation

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Author Biographies

Kavita Behara

Kavita Behara is a Senior Lecturer in the Department of Electrical Engineering at the Mangosuthu University of Technology, where she coordinates Computer Engineering. She is a Ph.D. candidate in the Discipline of Electrical, Electronics, and Computer Engineering at the University of KwaZulu-Natal, South Africa. She received her M.Sc. degree in computer science from Andhra University, India, in 2001. She has over 20 years of experience in teaching and managing academic programmes in higher education. Kavita is a certified IBM specialist for Application Security and Networks. Her research focuses on the application of AI. She worked on robotic projects with organizations (e.g., UKZN, Lego Robotics). She has participated as a keynote speaker in various conferences. Currently, her focus is on developing skin cancer detection systems using computer vision and image processing. She has memberships with IEEE, IITPSA, and INCOSE. In 2020, she won the Vice-Chancellors award for Excellence in T&L.

 

 

Ernest Bhero

Ernest Bhero is an electronic engineer by profession. He is also an advocate of the High Court of South Africa. He has worked as a chief design engineer for at least 4 years and has university lecturing experience spanning over 10 years. Both his honors and master’s degree projects were implemented in the industry. He is a hands-on person. He designed and developed at least four different electronic products that were commercialized in his country of birth. Recently, he designed and developed an electronic system, which is being tested in the industry by a company in South Africa. He has a deep interest not only in finding practical engineering solutions to engineering problems but also in relevant IP law.

 

John Terhile Agee

Prof Agee obtained his first degree in 1989 from the Abubakar Tafawa Balewa University (ATBU), Bauchi, Nigeria. After a year of National Service, Agee began his academic career at the ATBU as a Graduate Assistant in September 1990, where he Lectured until 2003. During his period at the ATBU, he rose to become a Senior Lecturer in 2001; and also obtained a Master’s degree (1992), a PhD (2000) at the ATBU, and also undertook a Postdoctoral Programme at the Ecoles des Mines de Paris, France. His further academic appointments include a Lecturer position at the University of Botswana (Jan. 2004-Dec. 2009); Associate of Professor of Control and Process Control at the Tshwane University of Technology, Pretoria (Dec. 2009-June 2015); and his current position at the University of KwaZulu Natal, where he has been since July 2015.

 

How to Cite

[1]
K. Behara, E. Bhero, and J. T. Agee, “Simulation-Based Evaluation of Dense Convolutional Neural Networks for Skin Cancer Detection”, IJEEI, vol. 13, no. 1, pp. 69–94, Feb. 2025, doi: 10.52549/ijeei.v13i1.5825.