ArsenicSkinImageBD: A comprehensive image dataset to classify affected and healthy skin of arsenic-affected people
Compared to other popular research domains, dermatology got less attention among machine learning researchers. One of the main concerns for this problem is an inadequate dataset since collecting samples from the human body is very sensitive. In recent years, arsenic has emerged as a significant issu...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-02-01
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| Series: | Data in Brief |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340923010430 |
| _version_ | 1797317101422116864 |
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| author | Ismot Ara Emu Nishat Tasnim Niloy Bhuyan Md Anowarul Karim Anindya Chowdhury Fatema Tuj Johora Mahamudul Hasan Tanni Mittra Mohammad Rifat Ahmmad Rashid Taskeed Jabid Maheen Islam Md. Sawkat Ali |
| author_facet | Ismot Ara Emu Nishat Tasnim Niloy Bhuyan Md Anowarul Karim Anindya Chowdhury Fatema Tuj Johora Mahamudul Hasan Tanni Mittra Mohammad Rifat Ahmmad Rashid Taskeed Jabid Maheen Islam Md. Sawkat Ali |
| author_sort | Ismot Ara Emu |
| collection | DOAJ |
| description | Compared to other popular research domains, dermatology got less attention among machine learning researchers. One of the main concerns for this problem is an inadequate dataset since collecting samples from the human body is very sensitive. In recent years, arsenic has emerged as a significant issue for dermatologists. Arsenic is a highly toxic substance found in the earth's crust whose small amounts can be very injurious to the human body. People who are exposed to arsenic for a long time through water and food can get cancer and skin lesions. With a view to contributing to this aspect, this dataset has been organized with the help of which the researchers can understand the impact of this contamination and design a solution using artificial intelligence. To the best of our knowledge, this is the first standard, easy-to-use, and open dataset of arsenic diseases. The images were collected from four places in Bangladesh, under the Department of Public Health Engineering, Chapainawabganj, where they are working on arsenic contamination. The dataset has 8892 skin images, with half of them showing people with arsenic effects and the other half showing mixed skin images that are not affected by arsenic. This makes the dataset useful for treating people with arsenic-related conditions. Eventually, this dataset can attract the attention of not only the machine learning researchers, but also scientists, doctors, and other professionals in the associated research field. |
| first_indexed | 2024-03-08T03:29:59Z |
| format | Article |
| id | doaj.art-17a122aa32244d37ab64160d2ed5468d |
| institution | Directory Open Access Journal |
| issn | 2352-3409 |
| language | English |
| last_indexed | 2024-03-08T03:29:59Z |
| publishDate | 2024-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj.art-17a122aa32244d37ab64160d2ed5468d2024-02-11T05:10:59ZengElsevierData in Brief2352-34092024-02-0152110016ArsenicSkinImageBD: A comprehensive image dataset to classify affected and healthy skin of arsenic-affected peopleIsmot Ara Emu0Nishat Tasnim Niloy1Bhuyan Md Anowarul Karim2Anindya Chowdhury3Fatema Tuj Johora4Mahamudul Hasan5Tanni Mittra6Mohammad Rifat Ahmmad Rashid7Taskeed Jabid8Maheen Islam9Md. Sawkat Ali10Department of Computer Science and Engineering, East West University, Aftabnagar, Dhaka, BangladeshCorresponding author.; Department of Computer Science and Engineering, East West University, Aftabnagar, Dhaka, BangladeshDepartment of Computer Science and Engineering, East West University, Aftabnagar, Dhaka, BangladeshDepartment of Computer Science and Engineering, East West University, Aftabnagar, Dhaka, BangladeshDepartment of Computer Science and Engineering, East West University, Aftabnagar, Dhaka, BangladeshDepartment of Computer Science and Engineering, East West University, Aftabnagar, Dhaka, BangladeshDepartment of Computer Science and Engineering, East West University, Aftabnagar, Dhaka, BangladeshDepartment of Computer Science and Engineering, East West University, Aftabnagar, Dhaka, BangladeshDepartment of Computer Science and Engineering, East West University, Aftabnagar, Dhaka, BangladeshDepartment of Computer Science and Engineering, East West University, Aftabnagar, Dhaka, BangladeshDepartment of Computer Science and Engineering, East West University, Aftabnagar, Dhaka, BangladeshCompared to other popular research domains, dermatology got less attention among machine learning researchers. One of the main concerns for this problem is an inadequate dataset since collecting samples from the human body is very sensitive. In recent years, arsenic has emerged as a significant issue for dermatologists. Arsenic is a highly toxic substance found in the earth's crust whose small amounts can be very injurious to the human body. People who are exposed to arsenic for a long time through water and food can get cancer and skin lesions. With a view to contributing to this aspect, this dataset has been organized with the help of which the researchers can understand the impact of this contamination and design a solution using artificial intelligence. To the best of our knowledge, this is the first standard, easy-to-use, and open dataset of arsenic diseases. The images were collected from four places in Bangladesh, under the Department of Public Health Engineering, Chapainawabganj, where they are working on arsenic contamination. The dataset has 8892 skin images, with half of them showing people with arsenic effects and the other half showing mixed skin images that are not affected by arsenic. This makes the dataset useful for treating people with arsenic-related conditions. Eventually, this dataset can attract the attention of not only the machine learning researchers, but also scientists, doctors, and other professionals in the associated research field.http://www.sciencedirect.com/science/article/pii/S2352340923010430Arsenic disease datasetDermatologyImage classificationArtificial intelligence modelSkin imageComputer vision |
| spellingShingle | Ismot Ara Emu Nishat Tasnim Niloy Bhuyan Md Anowarul Karim Anindya Chowdhury Fatema Tuj Johora Mahamudul Hasan Tanni Mittra Mohammad Rifat Ahmmad Rashid Taskeed Jabid Maheen Islam Md. Sawkat Ali ArsenicSkinImageBD: A comprehensive image dataset to classify affected and healthy skin of arsenic-affected people Data in Brief Arsenic disease dataset Dermatology Image classification Artificial intelligence model Skin image Computer vision |
| title | ArsenicSkinImageBD: A comprehensive image dataset to classify affected and healthy skin of arsenic-affected people |
| title_full | ArsenicSkinImageBD: A comprehensive image dataset to classify affected and healthy skin of arsenic-affected people |
| title_fullStr | ArsenicSkinImageBD: A comprehensive image dataset to classify affected and healthy skin of arsenic-affected people |
| title_full_unstemmed | ArsenicSkinImageBD: A comprehensive image dataset to classify affected and healthy skin of arsenic-affected people |
| title_short | ArsenicSkinImageBD: A comprehensive image dataset to classify affected and healthy skin of arsenic-affected people |
| title_sort | arsenicskinimagebd a comprehensive image dataset to classify affected and healthy skin of arsenic affected people |
| topic | Arsenic disease dataset Dermatology Image classification Artificial intelligence model Skin image Computer vision |
| url | http://www.sciencedirect.com/science/article/pii/S2352340923010430 |
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