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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
Compliance: UGC Journal Norms
License: CC BY 4.0
Peer Review: Double Blind
Volume 02, Issue 10

Published on: October 2026

AI-BASED SKIN DISEASE PREDICTION SYSTEM USING CONVNEXT-TINY WITH EXPLAINABLE VISUALIZATION AND PHASE GUIDANCE

Balkrishna Arun M Adarsh Arban Reyaz Khan

Dr. Syeda Ayesha Unisa

Dept. of Computer Science and Engineering

RV Institute of Technology and Management

Bengaluru, India
DOI:https://doi.org/10.55041/ijcope.v2i10.195

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Abstract

Skin diseases are common, but seeing a dermatologist is not easy for everyone, especially in under-served regions. Many deep-learning classifiers are heavy, hard to interpret and stop at a class label. We present MediCare AI, a lightweight screening system that sorts skin images into seven disease categories by fully fine-tuning an ImageNet-pretrained ConvNeXt-Tiny with AdamW, cosine learning-rate decay, label smoothing and early stopping. Gradient-weighted Class Activation Mapping shows which parts of the image drove each prediction. A Streamlit app accepts uploaded photos and live camera frames, reports the confidence and a rough three-level visible phase, and gives treatment suggestions and preventive advice through a generative service, with a local offline fallback. A single network reached roughly 80%–90% validation accuracy. The tool is meant for education and early screening, not as a replacement for professional diagnosis.

Index Terms—skin disease classification, ConvNeXt, transfer learning, Grad-CAM, explainable artificial intelligence, Streamlit.

How to Cite this Paper

Balkrishna, , M, A., Adarsh, & Khan, A. R. (2026). AI-Based Skin Disease Prediction System Using ConvNeXt-Tiny with Explainable Visualization and Phase Guidance. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(10), 1-9. https://doi.org/10.55041/ijcope.v2i10.195

Balkrishna, , et al.. "AI-Based Skin Disease Prediction System Using ConvNeXt-Tiny with Explainable Visualization and Phase Guidance." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 10, 2026, pp. 1-9. doi:https://doi.org/10.55041/ijcope.v2i10.195.

Balkrishna, ,Arun M, Adarsh, and Arban Khan. "AI-Based Skin Disease Prediction System Using ConvNeXt-Tiny with Explainable Visualization and Phase Guidance." International Journal of Creative and Open Research in Engineering and Management 02, no. 10 (2026): 1-9. https://doi.org/https://doi.org/10.55041/ijcope.v2i10.195.

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References

[1]     A. Esteva et al., “Dermatologist-level classification of skin cancer with deep neural networks,” Nature, vol. 542, no. 7639, pp. 115–118, Feb. 2017.

[2]     Z. R. Abdulqader and A. R. Abrahim, “Deep learning-based skin disease detection and classification: A systematic literature review,” Dasinya J. Eng. Informat., vol. 2, no. 1, 2026.

[3]     A. Aboulmira, H. Hrimech, and M. Lachgar, “Skin diseases classification with machine learning and deep learning techniques: A systematic review,” Int. J. Adv. Comput. Sci. Appl., vol. 15, no. 10, 2024.

[4]     M. Badr, A. Elkasaby, M. Alrahmawy, and S. El-Metwally, “A multi-model deep learning architecture for diagnosing multi-class skin diseases,” J. Imaging Informat. Med., vol. 38, pp. 1776–1795, 2025.

[5]     K. Behara, E. Bhero, and J. T. Agee, “An improved skin lesion classification using a hybrid approach with attention-guided capsule networks,” Diagnostics, vol. 14, no. 6, p. 636, 2024.

[6]     Y. Yang et al., “Skin disease classification using transfer learning model and fusion strategy,” Intell.-Based Med., vol. 12, 2025.

[7]     N. Tarisafitri and A. Aljabar, “Classification of skin diseases using digital image processing with MobileNetV2 architecture,” Nusantara J. Artif. Intell. Inf. Syst., vol. 1, no. 1, pp. 35–44, 2025.

[8]     F. Ahmmed et al., “Skin disease detection and classification of actinic keratosis and psoriasis utilizing deep transfer learning,” arXiv:2501.13713, Jan. 2025.

[9]     Z. Liu, H. Mao, C.-Y. Wu, C. Feichtenhofer, T. Darrell, and S. Xie, “A ConvNet for the 2020s,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), New Orleans, LA, USA, 2022, pp. 11976–11986.

[10]   R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Grad-CAM: Visual explanations from deep networks via gradient-based localization,” in Proc. IEEE Int. Conf. Comput. Vis. (ICCV), Venice, Italy, 2017, pp. 618–626.

Ethical Compliance & Review Process

  • •All submissions are screened under plagiarism detection.
  • •Review follows editorial policy.
  • •Authors retain copyright.
  • •Peer Review Type: Double-Blind Peer Review
  • •Published on: Oct 29 2026
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