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: | 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 |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2024-02-01
|
Series: | Data in Brief |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340923010430 |
Similar Items
-
Investigating the Mechanism of Arsenic-induced Ferroptosis in the Skin
by: Mehdi Koushki, et al.
Published: (2023-01-01) -
Design of a real-time crime monitoring system using deep learning techniques
by: Md. Muktadir Mukto, et al.
Published: (2024-03-01) -
Suitability of rainwater harvesting in saline and arsenic affected areas of Bangladesh
by: Md. Abdullah, et al.
Published: (2024-07-01) -
CottonFabricImageBD: An image dataset characterized by the percentage of cotton in a fabric for computer vision-based garment recycling
by: Nishat Tasnim Niloy, et al.
Published: (2024-08-01) -
Factors and Mechanisms Affecting Arsenic Migration in Cultivated Soils Irrigated with Contained Arsenic Brackish Groundwater
by: Wenjing Dai, et al.
Published: (2024-11-01)