Mehedi Md Humaion Kabir, Nafis Kh Fardin Zubair, Charu Krity Haque, Uddin Jia, Alam Md Golam Rabiul, Mridha M F
Department of Computer Science and Engineering, BRAC University, Dhaka, Bangladesh.
AI and Big Data Department, Endicott College, Woosong University, South Korea.
PLoS One. 2025 May 30;20(5):e0322405. doi: 10.1371/journal.pone.0322405. eCollection 2025.
Arsenic contamination of drinking water is a significant health risk. Countries such as Bangladesh's rural areas and regions are in the red alert zone because groundwater is the only primary source of drinking. Early detection of arsenic disease is critical for mitigating long-term health issues. However, these approaches are not widely accepted. In this study, we proposed a fusion approach for the detection of arsenic skin disease. The proposed model is a combination of the Xception model with the Inception module in a deep learning architecture named "ArsenicNet." The model was trained and tested on a publicly available image dataset named "ArsenicSkinImageBD" which contains only 1287 samples and is based on Bangladeshi people. The proposed model achieved the best accuracy through proper experimentation compared to several state-of-the-art deep learning models, including InceptionV3, VGG19, EfficientNetV2B0, ResNet152V2, ViT, and Xception. The proposed model achieved an accuracy of 97.69% and an F1 score of 97.63%, demonstrating superior performance. This research indicates that our proposed model can detect complex patterns in which arsenic skin disease is present, leading to a superior detection performance. Moreover, data augmentation techniques and earlystoping function were used to prevent models overfitting. This study highlights the potential of sophisticated deep learning methodologies to enhance the accuracy of arsenic detection and prevent premature interventions in the diagnosis of arsenic-related illnesses in people. This research contributes to ongoing efforts to develop robust and scalable solutions to monitor and manage arsenic contamination-related health issues.
饮用水中的砷污染是一个重大的健康风险。孟加拉国农村地区等国家处于红色警戒区，因为地下水是唯一的主要饮用水源。早期发现砷中毒疾病对于减轻长期健康问题至关重要。然而，这些方法并未被广泛接受。在本研究中，我们提出了一种用于检测砷皮肤病的融合方法。所提出的模型是在名为“ArsenicNet”的深度学习架构中，将Xception模型与Inception模块相结合。该模型在一个名为“ArsenicSkinImageBD”的公开可用图像数据集上进行训练和测试，该数据集仅包含1287个样本，且以孟加拉国人为基础。与包括InceptionV3、VGG19、EfficientNetV2B0、ResNet152V2、ViT和Xception在内的几种先进深度学习模型相比，通过适当的实验，所提出的模型取得了最佳准确率。所提出的模型实现了97.69%的准确率和97.63%的F1分数，表现出卓越的性能。这项研究表明，我们提出的模型可以检测出存在砷皮肤病的复杂模式，从而实现卓越的检测性能。此外，使用了数据增强技术和提前停止函数来防止模型过度拟合。本研究突出了先进深度学习方法在提高砷检测准确性以及防止对与砷相关疾病的人群诊断进行过早干预方面的潜力。这项研究有助于持续努力开发强大且可扩展的解决方案，以监测和管理与砷污染相关的健康问题。
