[论文解读] An Image Dataset of Common Skin Diseases of Bangladesh and Benchmarking Performance with Machine Learning Models

Md. Sazzad Hossain, Saiful Islam+4 more|arXiv (Cornell University)|Mar 26, 2026

Cutaneous Melanoma Detection and Management被引用 0

一句话总结

该论文提供了一个公开可用的孟加拉国五种常见皮肤病的图像数据集，并在其上对多种机器学习/深度学习模型进行了基准测试。同时讨论了数据集的收集与区域相关性对全球皮肤科应用的意义。

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.

研究动机与目标

• 促使在皮肤科专科资源有限的孟加拉国实现皮肤病自动检测。
• 创建一个公开可用的聚焦于常见皮肤病的图像数据集。
• 在数据集上对多种机器学习与深度学习模型进行基准测试，以确立基线性能。
• 强调该数据集在全球范围内用于基于ML的皮肤科研究的潜在相关性。

提出的方法

• 从孟加拉国医学院附属医院的门诊患者中组建区域性皮肤病图像数据集。
• 将图像分为五种疾病：接触性皮炎、白斑（Vitiligo）、湿疹、疥癣、癣性环丝虫病。
• 提供数据集组成信息，包括增强图像与独立图像的比例（总计1612张，其中独立图像250张）。
• 应用若干机器学习和深度学习模型进行疾病分类并报告性能。
• 讨论该数据集在低资源环境中用于ML驱动的皮肤病研究的潜在适用性。

实验结果

研究问题

• RQ1 公开可用的孟加拉国常见皮肤病图像数据集是否能支持基于ML的检测？
• RQ2 在五个疾病类别上，不同的ML/DL模型在此数据集上的表现如何？
• RQ3 数据增强对皮肤病分类模型性能的影响是什么？
• RQ4 该数据集是否在全球皮肤科应用中具有潜在价值，超越区域背景？

主要发现

• 数据集包含五种疾病共1612张图像：Dermatitis（302）、Eczema（381）、Scabies（301）、Tinea Ringworm（316）、Vitiligo（312）。
• 其中250张为独立图像；其余为增强以扩展数据集。
• 在该数据集上对一系列机器学习和深度学习模型进行了基准测试，但摘要未提供具体的分类指标。
• 作者认为该数据集的区域性收集仍可能对全球应用在ML驱动的皮肤科研究中具有价值。