Hair and Scalp Disease Detection using Machine Learning and Image Processing

Mrinmoy Roy and Anica Tasnim Protity
Additional contact information
Mrinmoy Roy: Northern Illinois University, USA
Anica Tasnim Protity: Northern Illinois University, USA

European Journal of Information Technologies and Computer Science, 2023, vol. 3, issue 1, 7-13

Abstract: Almost 80 million Americans suffer from hair loss due to aging, stress, medication, or genetic makeup. Hair and scalp-related diseases often go unnoticed in the beginning. Sometimes, a patient cannot differentiate between hair loss and regular hair fall. Diagnosing hair-related diseases is time-consuming as it requires professional dermatologists to perform visual and medical tests. Because of that, the overall diagnosis gets delayed, which worsens the severity of the illness. Due to the image-processing ability, neural network-based applications are used in various sectors, especially healthcare and health informatics, to predict deadly diseases like cancers and tumors. These applications assist clinicians and patients and provide an initial insight into early-stage symptoms. In this study, we used a deep learning approach that successfully predicts three main types of hair loss and scalp-related diseases: alopecia, psoriasis, and folliculitis. However, limited study in this area, unavailability of a proper dataset, and degree of variety among the images scattered over the internet made the task challenging. 150 images were obtained from various sources and then preprocessed by denoising, image equalization, enhancement, and data balancing, thereby minimizing the error rate. After feeding the processed data into the 2D convolutional neural network (CNN) model, we obtained overall training accuracy of 96.2%, with a validation accuracy of 91.1%. The precision and recall score of alopecia, psoriasis, and folliculitis are 0.895, 0.846, and 1.0, respectively. We also created a dataset of the scalp images for future prospective researchers.

Keywords: Deep learning; health informatics; machine learning; scalp/ hair diseases (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
https://eu-opensci.org/index.php/compute/article/view/10085 Abstract page (text/html)
https://eu-opensci.org/index.php/compute/article/download/10085/1812 Full text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:epw:comput:v:3:y:2023:i:1:id:10085

DOI: 10.24018/compute.2023.3.1.85

Access Statistics for this article

More articles in European Journal of Information Technologies and Computer Science from European Open Science
Bibliographic data for series maintained by Support Team ().