Automated Dermatological Diagnosis Utilizing Convolutional Neural Networks: A Comparative Analysis

Anchal Kumari; Punam Rattan

doi:10.48084/etasr.17408

Authors

Anchal Kumari Lovely Professional University, Jalandhar, India
Punam Rattan Manav Rachna University, Faridabad, Haryana, India

Volume: 16 | Issue: 3 | Pages: 35975-35980 | June 2026 | https://doi.org/10.48084/etasr.17408

Received: 8 January 2026 | Revised: 18 February 2026 | Accepted: 8 March 2026 | Online: 2 April 2026

Corresponding author: Anchal Kumari

Abstract

The incidence of fatalities due to skin cancer is increasing, rendering it a significant public health concern. Computer-aided diagnosis tools are increasingly being utilized to enhance the accuracy of skin cancer diagnoses. This study seeks to establish a dependable approach for skin cancer detection employing machine learning algorithms to classify images using Convolutional Neural Networks (CNNs), a type of deep learning that is known for its efficiency and accuracy. This study identified skin cancer images with pre-trained DenseNet169, VGG16, MobileNetV2, and Xception architectures. The accuracy performance of these models was 91.2% for DenseNet169, 88.13% for VGG16, 77.86% for MobileNetV2, and 94% for Xception. These deep-learning algorithms can assist dermatologists in diagnosing skin cancer with more accuracy and mitigate errors attributable to human oversight.

Keywords:

skin cancer, VGG16, deep learning, Xception

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