Skin Disease Type Detector Using Deep Learning

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@article{1716483,
      author = {Parth Sharma, Nilesh Kumar Pandey, Dr. Ishrat Ali, Dr. Sanjay Pachauri},
      title = {Skin Disease Type Detector Using Deep Learning},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {10},
      pages = {2016-2018},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1716483.pdf},
      abstract = {The exponential rise in skin diseases across the globe has created an urgent demand for automated, scalable, and precise diagnostic tools. In this paper, we present a deep learning-based Skin Disease Type Detector that leverages Convolutional Neural Networks (CNNs) and transfer learning to classify multiple types of skin conditions from dermoscopic and clinical images. The system is built on a modular image processing pipeline that pre-processes skin lesion images, extracts spatial and textural features through deep neural layers, and produces accurate multi-class predictions with confidence scores. Our implementation integrates a user-friendly interface that allows clinicians and patients to upload images and receive instant, cited predictions grounded in medical image databases. Experimental results demonstrate significant improvements in classification accuracy when compared to conventional machine learning baselines, while also reducing misdiagnosis rates. The system is designed to be domain-adaptive and can be extended to accommodate new disease categories through transfer learning without retraining from scratch. This work represents a meaningful step toward building trustworthy AI systems that combine generative fluency with clinical reliability for dermatological diagnosis.},
      keywords = {Skin Disease Detection, Deep Learning, Convolutional Neural Networks, Transfer Learning, Image Classification, Dermoscopy, Medical AI, HAM10000, ResNet, EfficientNet.},
      month = {April},
      doi = {https://doi.org/10.64388/IREV9I10-1716483}
  }