提出模型的超参数
包含ArsenicNet模型用于检测砷皮肤疾病的超参数配置，基于1287张图像训练，准确率达97.69%。
基本信息
资源简介
提出模型的超参数
【数据集背景】
本数据集源站标题为《Hyperparameters for proposed model.》。源站首次发布：2025-05-30。
【数据内容】
源站原始描述：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…
【数据结构与技术规格】
文件数 1 个，合计 5.63 KB。Table 3.xls：application/vnd.ms-excel，5.63 KB。数据集 DOI：10.1371/journal.pone.0322405.t003。
【主题与分类】
源站学科分类：Biotechnology、Biological Sciences not elsewhere classified、Information Systems not elsewhere classified、Cancer、Science Policy。主题标签：term health issues、significant health risk、red alert zone、proper experimentation compared、prevent premature interventions、prevent models overfitting、detect complex patterns、data augmentation techniques、bangladesh &# 8217、manage arsenic contamination。
【适用方向】
结构化表格数据可直接用于统计建模、特征工程与队列分析。
【获取与许可】
源站页面：https://plos.figshare.com/articles/dataset/Hyperparameters_for_proposed_model_/29201735
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Mehedi, Md Humaion Kabir; Nafis, Kh. Fardin Zubair; Charu, Krity Haque; Uddin, Jia; Alam, Md Golam Rabiul; Mridha, M.F. (2025). Hyperparameters for proposed model.. PLOS ONE. Dataset
下载信息
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使用方式
数据集获取（Figshare）
- 源站标题：Hyperparameters for proposed model.
- DOI：10.1371/journal.pone.0322405.t003
- 发布日期：2025-05-30
- 文件数：1 个
- 文件总体积：5.63 KB
- 源站页面：https://plos.figshare.com/articles/dataset/Hyperparameters_for_proposed_model_/29201735
命令行下载
curl -L -o "Table 3.xls" "https://ndownloader.figshare.com/files/55000898"
文件清单
Table 3.xls（5.63 KB）
文件总体积 5.63 KB（源站 API 实测）。
数据说明
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.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0322405.t003（source: https://plos.figshare.com/articles/dataset/Hyperparameters_for_proposed_model_/29201735）。
数据缺失？
依托陕西、四川两大基地，我们与超过十家三甲医院建立直接合作关系，覆盖合规授权、采集治理、专业标注、数据交付的全流程，为AI医疗团队提供即拿即用的高质量临床数据。
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