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Attention-Enhanced Deep Learning Framework for Automated Skin Disease Classification

by Nasrin Sultana, Samia Tabassum Hrity, Shah Tanzeem Afsar, Shahriar Arefin Zummon
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 137
Year of Publication: 2026
Authors: Nasrin Sultana, Samia Tabassum Hrity, Shah Tanzeem Afsar, Shahriar Arefin Zummon
10.5120/ijcaedf9f1ac9e27

Nasrin Sultana, Samia Tabassum Hrity, Shah Tanzeem Afsar, Shahriar Arefin Zummon . Attention-Enhanced Deep Learning Framework for Automated Skin Disease Classification. International Journal of Computer Applications. 187, 137 ( Aug 2026), 1-6. DOI=10.5120/ijcaedf9f1ac9e27

@article{ 10.5120/ijcaedf9f1ac9e27,
author = { Nasrin Sultana, Samia Tabassum Hrity, Shah Tanzeem Afsar, Shahriar Arefin Zummon },
title = { Attention-Enhanced Deep Learning Framework for Automated Skin Disease Classification },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2026 },
volume = { 187 },
number = { 137 },
month = { Aug },
year = { 2026 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number137/attention-enhanced-deep-learning-framework-for-automated-skin-disease-classification/ },
doi = { 10.5120/ijcaedf9f1ac9e27 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2026-08-20T21:55:08.654127+05:30
%A Nasrin Sultana
%A Samia Tabassum Hrity
%A Shah Tanzeem Afsar
%A Shahriar Arefin Zummon
%T Attention-Enhanced Deep Learning Framework for Automated Skin Disease Classification
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 137
%P 1-6
%D 2026
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Skin disease is the most common health problem worldwide. It affects millions of people and often impacts both physical health and quality of life. Early detection of skin conditions is essential to prevent complications and improve treatment outcomes. The availability of Dermatologists is limited in many regions particularly in rural and underserved areas. As a result, patients often experience delays in diagnosis and treatment. In recent times, advances in artificial intelligence, especially deep learning have shown great potential in medical image analysis and automated illness detection. This research presents deep learning based framework for automated skin disease detection using dermoscopic images and incorporates the trained model into a mobile app application for practical use. The suggested system is designed to assists users in identifying possible skin conditions in an early stage that encourages timely medical consultation. This model is based in an EfficientNetV2-S convolutional neural network. Additionally Convolutional Block Attention Module (CBAM) and Generalized Mean (GeM) pooling are used. Six common skin disease categories were trained and evaluated on a dataset. A two-stage training strategy was adopted. Experimental results reveal that the suggested model could efficiently learns visual patterns that is associated with several dermatological disorders, achieving a final classification accuracy of 91.77% and a macro F1-score of 0.8548 across the evaluated disease categories. A mobile healthcare application was constructed for real-time uses. This enables users to shoot or upload skin photos and get real-time analysis provided by the trained model.

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Index Terms

Computer Science
Information Sciences

Keywords

CBAM Computer-aided diagnosis Dermoscopy EfficientNetV2 GeM pooling Image classification Skin disease detection

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