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International Journal of Creative and Open Research in Engineering and Management

A Peer-Reviewed, Open-Access International Journal Supporting Multidisciplinary Research, Digital Publishing Standards, DOI Registration, and Academic Indexing.
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ISSN: 3108-1754 (Online)
Crossref DOI: Available
ISO Certification: 9001:2015
Publication Fee: 599/- INR
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License: CC BY 4.0
Peer Review: Double Blind
Volume 02, Issue 9

Published on: September 2026

AI-BASED SKIN DISEASE DETECTION AND CLASSIFICATION USING DEEP LEARNING

Pranali Bhanarkar Chhakuli Naktode Pranjali Nagose Trupti Navghare

Dr. Sushama Telrandhe

Dept. of CSE, Gurunanak Institute of Engineeringand Technology, Nagpur, Maharashtra, India

Article Status

Plagiarism Passed Peer Reviewed Open Access

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Abstract

Skin diseases are common health problems that affect people across different age groups and geographical regions. Early and accurate identification is important because delayed diagnosis or inappropriate treatment may lead to complications. This paper presents an AI-based Skin Disease Detection and Classification System that analyzes digital skin images and provides preliminary information about possible skin conditions. The proposed system uses image preprocessing operations such as resizing, pixel normalization, noise handling, and image standardization to improve input consistency. EfficientNetB0 is used as the primary deep learning model with transfer learning to extract meaningful visual features and classify predefined skin disease categories. The predicted disease and confidence score are displayed through an Android-based application. The application is developed using Android Studio, Java, and XML, while the model is trained using Python, TensorFlow, and Keras and deployed using TensorFlow Lite. A product recommendation module maps the detected condition to a predefined recommendation database containing product category, ingredients, intended skin concern, usage information, and safety precautions. The proposed system is intended for preliminary screening and skin-disease awareness and is not a replacement for professional medical diagnosis.

Keywords: Convolutional Neural Networks (CNN), Deep Learning, Skin Disease Detection, EfficientNetB0, HAM10000, Transfer Learning, Image Preprocessing, TensorFlow Lite, Android Application

How to Cite this Paper

Bhanarkar, P., Naktode, C., Nagose, P. & Navghare, T. (2026). AI-Based Skin Disease Detection and Classification Using Deep Learning. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(9), 1-9. https://doi.org/10.55041/ijcope.v2i9.002

Bhanarkar, Pranali, et al.. "AI-Based Skin Disease Detection and Classification Using Deep Learning." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 9, 2026, pp. 1-9. doi:https://doi.org/10.55041/ijcope.v2i9.002.

Bhanarkar, Pranali,Chhakuli Naktode,Pranjali Nagose, and Trupti Navghare. "AI-Based Skin Disease Detection and Classification Using Deep Learning." International Journal of Creative and Open Research in Engineering and Management 02, no. 9 (2026): 1-9. https://doi.org/https://doi.org/10.55041/ijcope.v2i9.002.

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References


  • Tschandl, C. Rosendahl, and H. Kittler, “The HAM10000 Dataset: A Large Collection of Multi-Source Dermatoscopic Images of Common Pigmented Skin Lesions,” Scientific Data, vol. 5, 2018.

  • C. F. Codella et al., “Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI),” IEEE International Symposium on Biomedical Imaging (ISBI).

  • Tan and Q. V. Le, “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks,” in Proceedings of the 36th International Conference on Machine Learning (ICML), 2019.

  • He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.

  • Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “MobileNetV2: Inverted Residuals and Linear Bottlenecks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.

  • V. Telrandhe, “AI-Based Skin Disease Detection and Classification Using Deep Learning,” International Journal of Computer Information Systems and Industrial Management


 

Applications, vol. 18, no. 3s, pp. 261–272, 2026, doi: 10.70917/ijcisim-2026-2328.

  • Venu Gopal, A. S. H. N. Pavan, K. Nagendra, M. Pavan Sai, and A. Vijay Kumar, “Skin Disease Detection Using Deep Learning Techniques,” Journal of Prevention, Diagnosis and Management of Human Diseases, vol. 4, no. 1, pp. 40–49, 2024, doi: 10.55529/jpdmhd.41.40.49.

  • Ahmad, M. Saleem, J. A. Malik, W. A. Bukhari, M. I. Kashif, H. Salahuddin, M. A. U. Rehman, and A. U. Rehman, “Mobile Application for Skin Disease Classification Using CNN with User Privacy,” Journal of Computing & Biomedical Informatics, 2024.

  • Rezaee and H. Ghayoumi Zadeh, “Self-attention Transformer Unit-based Deep Learning Framework for Skin Lesions Classification in Smart Healthcare,” Discover Applied Sciences, vol. 6, article 3, 2024.

  • P. Yadav, B. Sharma, S. Chauhan, J. L. Webber, and



  1. Mehbodniya, “Dual Scale Lightweight Cross Attention Transformer for Skin Lesion Classification,” PLOS ONE, vol. 19, 2024, doi: 10.1371/journal.pone.0312598.

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  • Published on: Sep 03 2026
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