An Image Dataset of Common Skin Diseases of Bangladesh and Benchmarking Performance with Machine Learning Models

2026-03-26

AI Summary

This study addresses the diagnostic challenges of common skin diseases in resource-limited regions like Bangladesh, where a shortage of dermatologists impedes timely care. To this end, the authors present the first publicly available skin image dataset tailored to locally prevalent conditions—specifically contact dermatitis, vitiligo, eczema, scabies, and tinea—comprising 1,612 images, including 250 newly collected clinical photographs. The work establishes baseline classification performance on this dataset through systematic application of image augmentation techniques, computer vision methods, and a range of machine learning and deep learning models. Designed with strong regional relevance yet broad global applicability, the dataset offers a critical resource and methodological foundation for advancing automated dermatological diagnosis in low-resource settings.

Technology Category

• Computer Vision: Bias, Fairness & Privacy
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Machine Learning: Other Foundations of Machine Learning

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Large pretrained models with web data
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

Skin diseases are a major public health concern worldwide, and their detection is often challenging without access to dermatological expertise. In countries like Bangladesh, which is highly populated, the number of qualified skin specialists and diagnostic instruments is insufficient to meet the demand. Due to the lack of proper detection and treatment of skin diseases, that may lead to severe health consequences including death. Common properties of skin diseases are, changing the color, texture, and pattern of skin and in this era of artificial intelligence and machine learning, we are able to detect skin diseases by using image processing and computer vision techniques. In response to this challenge, we develop a publicly available dataset focused on common skin disease detection using machine learning techniques. We focus on five prevalent skin diseases in Bangladesh: Contact Dermatitis, Vitiligo, Eczema, Scabies, and Tinea Ringworm. The dataset consists of 1612 images (of which, 250 are distinct while others are augmented), collected directly from patients at the outpatient department of Faridpur Medical College, Faridpur, Bangladesh. The data comprises of 302, 381, 301, 316, and 312 images of Dermatitis, Eczema, Scabies, Tinea Ringworm, and Vitiligo, respectively. Although the data are collected regionally, the selected diseases are common across many countries especially in South Asia, making the dataset potentially valuable for global applications in machine learning-based dermatology. We also apply several machine learning and deep learning models on the dataset and report classification performance. We expect that this research would garner attention from machine learning and deep learning researchers and practitioners working in the field of automated disease diagnosis.

Problem

• skin diseases
• diagnostic shortage
• public health
• automated diagnosis
• dermatology

Innovation

• skin disease dataset
• machine learning benchmarking
• medical image augmentation
• dermatology AI
• South Asia healthcare