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ArsenicSkinImageBD: A comprehensive image dataset to classify affected and healthy skin of arsenic-affected people.
Emu, Ismot Ara; Niloy, Nishat Tasnim; Karim, Bhuyan Md Anowarul; Chowdhury, Anindya; Johora, Fatema Tuj; Hasan, Mahamudul; Mittra, Tanni; Rashid, Mohammad Rifat Ahmmad; Jabid, Taskeed; Islam, Maheen; Ali, Md Sawkat.
Afiliación
  • Emu IA; Department of Computer Science and Engineering, East West University, Aftabnagar, Dhaka, Bangladesh.
  • Niloy NT; Department of Computer Science and Engineering, East West University, Aftabnagar, Dhaka, Bangladesh.
  • Karim BMA; Department of Computer Science and Engineering, East West University, Aftabnagar, Dhaka, Bangladesh.
  • Chowdhury A; Department of Computer Science and Engineering, East West University, Aftabnagar, Dhaka, Bangladesh.
  • Johora FT; Department of Computer Science and Engineering, East West University, Aftabnagar, Dhaka, Bangladesh.
  • Hasan M; Department of Computer Science and Engineering, East West University, Aftabnagar, Dhaka, Bangladesh.
  • Mittra T; Department of Computer Science and Engineering, East West University, Aftabnagar, Dhaka, Bangladesh.
  • Rashid MRA; Department of Computer Science and Engineering, East West University, Aftabnagar, Dhaka, Bangladesh.
  • Jabid T; Department of Computer Science and Engineering, East West University, Aftabnagar, Dhaka, Bangladesh.
  • Islam M; Department of Computer Science and Engineering, East West University, Aftabnagar, Dhaka, Bangladesh.
  • Ali MS; Department of Computer Science and Engineering, East West University, Aftabnagar, Dhaka, Bangladesh.
Data Brief ; 52: 110016, 2024 Feb.
Article en En | MEDLINE | ID: mdl-38293578
ABSTRACT
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.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Idioma: En Revista: Data Brief Año: 2024 Tipo del documento: Article País de afiliación: Bangladesh

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Idioma: En Revista: Data Brief Año: 2024 Tipo del documento: Article País de afiliación: Bangladesh