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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: February 2026
Volume 11 | Issue 2
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 117
Article Submitted : 21318
Article Published : 8476
Total Authors : 22301
Total Reviewer : 802
Total Countries : 156
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| Paper Title: | Analysis of Human Hair Follicles in Scapls Using AIML and Image Processing |
| Authors Name: | Srihari A , Kaleeswaran M , Manimaran C , S Guruprasath |
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IJRTI_209231
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| Published Paper Id: | IJRTI2601059 |
| Published In: | Volume 11 Issue 1, January-2026 |
| DOI: | |
| Abstract: | Hair and scalp disorders have become increasingly prevalent due to modern lifestyle changes, stress, pollution, and improper hair care practices. Early diagnosis plays a vital role in preventing severe hair loss and scalp damage. However, conventional diagnosis methods rely on manual inspection by dermatologists, which is time-consuming, subjective, and costly. This project presents an automated system for detecting and classifying human scalp and hair disorders using Artificial Intelligence (AI), Machine Learning (ML), and Image Processing techniques. The proposed system uses image preprocessing techniques such as Contrast Limited Adaptive Histogram Equalization (CLAHE), noise removal, and image sharpening to enhance scalp images. A Convolutional Neural Network (CNN) based deep learning model, MobileNetV2, is employed for classification due to its lightweight architecture and high computational efficiency. The dataset used for training and testing consists of multiple classes of scalp conditions collected from a publicly available Kaggle dataset. Experimental results demonstrate a high classification accuracy of 99.8%, indicating the effectiveness of the proposed approach.The system has potential applications in early diagnosis, dermatological assistance, and telemedicine platforms. Future work includes deploying the model as a fully functional web application and expanding the dataset for improved robustness. |
| Keywords: | Scalp Disease Detection, Deep Learning, MobileNetV2, Image Processing, AI in Healthcare |
| Cite Article: | "Analysis of Human Hair Follicles in Scapls Using AIML and Image Processing", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.11, Issue 1, page no.a430-a436, January-2026, Available :http://www.ijrti.org/papers/IJRTI2601059.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2601059
Registration ID:209231
Published In: Volume 11 Issue 1, January-2026
DOI (Digital Object Identifier):
Page No: a430-a436 Country: Kovilpatti, Tamilnadu, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2601059 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2601059 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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