• Neha Kosthi
• Dr. Vishal Singh Chouhan,
• Dr. Navdeep Kaur Saluja

Skin disorders affect numerous individuals globally and pose a significant health concern. The effectiveness of treatment for various conditions relies on early and accurate diagnosis. This study employs deep learning techniques executed in MATLAB, particularly utilizing the VGG16 architecture, to provide a robust solution for the classification of skin diseases. The primary objective of this research is to develop a highly accurate and efficient model for the automated classification of skin conditions. The dataset for this project consists of five distinct categories of skin disorders: vitiligo, acute eczema, diabetic ulcers, insect bites, and cystic acne. Each class in the dataset has been meticulously selected to represent a variety of skin conditions, ensuring that the model is adaptable and capable of addressing a wide range of dermatological issues. The VGG16 architecture is a well-established convolutional neural network (CNN) model known for its exceptional feature extraction capabilities. By utilizing the rash dataset, transfer learning is applied to enhance the pre-trained VGG16 model. To ensure the model's reliability, a comprehensive cross-validation process is employed for training, validation, and testing. The remarkable classification accuracy achieved in this study is attributed to its advanced capabilities. With an impressive accuracy rate of 98.08%, the model effectively demonstrates its proficiency in diagnosing and classifying skin conditions. This high accuracy rate is crucial for preventing misdiagnoses and improving the quality of care for patients simultaneously. In addition to its exceptional accuracy, the proposed method provides dermatologists and healthcare professionals with real-time skin classification, making it an invaluable resource. A user-friendly interface developed with MATLAB ensures accessibility and practical utility, allowing healthcare professionals to make informed decisions quickly and accurately. In summary, this project presents a comprehensive framework for the classification of skin disorders.

- SVM, Vehicle Collision (AVC), labeling, neural network, Segmentation, tracking, Animal Footprint, Animal.

"Skin Disease Identification and Classification Using VGG16-Based Deep Learning Models", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 1, page no.c531-c539, January-2026, Available :http://www.jetir.org/papers/JETIR2601264.pdf

"Skin Disease Identification and Classification Using VGG16-Based Deep Learning Models", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 1, page no. ppc531-c539, January-2026, Available at : http://www.jetir.org/papers/JETIR2601264.pdf