Vol. 3 No. 1 (2025): Issue - 1
Articles

Deep Learning-Based Detection of Hair and Scalp Diseases Using CNN and Image Processing

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  • Busireddy Seshakagari Haranadha Reddy
Busireddy Seshakagari Haranadha Reddy
Manager – Architecture, Valuemomentum, Erie, PA, USA 16506.
DOI: https://doi.org/10.5281/zenodo.14965660

Published 2025-03-04

Keywords

  • Hair and Scalp Disorders,
  • Deep Learning,
  • Medical Image Processing,
  • Convolutional Neural Networks (CNNs),
  • Image Pre-processing Techniques,
  • Skin and Scalp Analysis
    • ...More
    Less

How to Cite

Busireddy Seshakagari Haranadha Reddy. (2025). Deep Learning-Based Detection of Hair and Scalp Diseases Using CNN and Image Processing. Milestone Transactions on Medical Technometrics, 3(1), 145–155. https://doi.org/10.5281/zenodo.14965660

Abstract

Hair and scalp disorders affect millions worldwide, often undiagnosed in their early stages due to similarities with normal hair shedding. Conditions such as alopecia, psoriasis, and folliculitis require professional dermatological evaluation, which can be time-consuming and lead to delayed treatment. Early diagnosis and clinical decision-making have been strengthened by automating disease detection in healthcare through the development of deep learning and improved image-processing techniques. This study evaluated the three primary conditions associated with the scalp using a convolutional neural network (CNN) model. The research faced challenges due to the restricted availability of structured datasets and variations in image quality from several sources. To address this, 150 images were gathered from various repositories and subjected to pre-processing techniques, including denoising, contrast enhancement, image equalization, and data balancing. The processed dataset was employed to train a 2D CNN model with a training and validation accuracy of 97% and 92%. Additionally, we curated a dataset of scalp images to support further research in this domain, promoting the development of more robust automated diagnostic systems.
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References

  1. Banerjee, D., Kukreja, V., Bordoloi, D., & Choudhary, A. (2024). Enhanced hair disease classification using deep learning. In 2024 11th International Conference on Signal Processing and Integrated Networks (SPIN) (pp. 58–63). IEEE.
  2. Polak-Witka, K., Rudnicka, L., Blume-Peytavi, U., & Vogt, A. (2020). The role of the microbiome in scalp hair follicle biology and disease. Experimental Dermatology, 29(3), 286–294.
  3. Dinnes, J., Deeks, J. J., Chuchu, N., di Ruffano, L. F., Matin, R. N., Thomson, D. R., Wong, K. Y., Aldridge, R. B., Abbott, R., & Fawzy, M. (2018). Dermoscopy, with and without visual inspection, for diagnosing melanoma in adults. Cochrane Database of Systematic Reviews, 12.
  4. Zhu, H., Isikman, S. O., Mudanyali, O., Greenbaum, A., & Ozcan, A. (2013). Optical imaging techniques for point-of-care diagnostics. Lab on a Chip, 13(1), 51–67.
  5. Ahmad, Z., Rahim, S., Zubair, M., & Abdul-Ghafar, J. (2021). Artificial intelligence (AI) in medicine, current applications and future role with special emphasis on its potential and promise in pathology: Present and future impact, obstacles including costs and acceptance among pathologists, practical and philosophical considerations. A comprehensive review. Diagnostic Pathology, 16, 1–16.
  6. Iqbal, M. I., & Kaushik, P. (2025). Deep learning for disease detection—A deep dive into deep learning techniques such as convolutional neural networks (CNNs) and their use in disease detection. AI in Disease Detection: Advancements and Applications, 99–122.
  7. Lee, A. K. W., Chan, L. K. W., Lee, C. H., Bohórquez, J. M. C., Haykal, D., Wan, J., & Yi, K.-H. (2025). Artificial intelligence application in diagnosing, classifying, localizing, detecting and estimating the severity of skin conditions in aesthetic medicine: A review. Dermatological Reviews, 6(1), e70015.
  8. Chowdhury, M. S., Sultan, T., Jahan, N., Mridha, M. F., Safran, M., Alfarhood, S., & Che, D. (2024). Leveraging deep neural networks to uncover unprecedented levels of precision in the diagnosis of hair and scalp disorders. Skin Research and Technology, 30(4), e13660.
  9. Shakeel, C. S., Khan, S. J., Chaudhry, B., Aijaz, S. F., & Hassan, U. (2021). Classification framework for healthy hairs and alopecia areata: A machine learning (ML) approach. Computational and Mathematical Methods in Medicine, 2021(1), 1102083.
  10. Kapoor, I., & Mishra, A. (2018). Automated classification method for early diagnosis of alopecia using machine learning. Procedia Computer Science, 132, 437–443.
  11. ALEnezi, N. S. A. (2019). A method of skin disease detection using image processing and machine learning. Procedia Computer Science, 163, 85–92.
  12. Hameed, N., Shabut, A. M., & Hossain, M. A. (2018). Multi-class skin diseases classification using deep convolutional neural network and support vector machine. In 2018 12th International Conference on Software, Knowledge, Information Management & Applications (SKIMA) (pp. 1–7). IEEE.
  13. Roy, M., & Protity, A. T. (2022). Hair and scalp disease detection using machine learning and image processing. arXiv preprint arXiv:2301.00122.
  14. Debbarma, T., Pal, T., Saha, A., & Debbarma, N. (2025). HCNNet: A hybrid convolutional neural network for abnormal human driver behavior detection. Sādhanā, 50(1), 9.
  15. Gür, Y. E., Toğaçar, M., & Solak, B. (2025). Integration of CNN models and machine learning methods in credit score classification: 2D image transformation and feature extraction. Computational Economics, 1–45.
  16. Madapuri, R. K., & Senthil Mahesh, P. C. (2017). HBS-CRA: Scaling impact of change request towards fault proneness: Defining a heuristic and biases scale (HBS) of change request artifacts (CRA). Cluster Computing, 22(S5), 11591–11599. https://doi.org/10.1007/s10586-017-1424-0
  17. Dwaram, J. R., & Madapuri, R. K. (2022). Crop yield forecasting by long short‐term memory network with Adam optimizer and Huber loss function in Andhra Pradesh, India. Concurrency and Computation: Practice and Experience, 34(27). https://doi.org/10.1002/cpe.7310
  18. Thulasi, M. S., Sowjanya, B., Sreenivasulu, K., & Kumar, M. R. (2022). Knowledge attitude and practices of dental students and dental practitioners towards artificial intelligence. International Journal of Intelligent Systems and Applications in Engineering, 10(1s), 248–253.