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@article{201041,
        author = {ANURAG KAILAS JADHAV and PRIYANKA PATIL and ROHAN PANCHAL and AKASH PATIL},
        title = {SKIN DISEASE DETECTION USING CNN},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {4885-4889},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=201041},
        abstract = {Dermatology is an essential branch of bioscience that deals with the diagnosis and treatment of skin disorders and allergies arising from biological and environmental factors. However, the human skin’s complex and dynamic nature makes analyzing, modeling, and differentiating between skin diseases a challenging task. The unpredictable behavior of skin conditions often leads to diagnostic complications, especially in areas lacking access to dermatologists or advanced medical infrastructure. Consequently, individuals in remote regions may ignore initial symptoms, resulting in severe complications.
To overcome these limitations, researchers are adopting artificial intelligence (AI) and computer vision techniques to automate the detection of skin diseases. Automated systems offer fast, accurate, and cost-effective diagnostic solutions, enhancing healthcare accessibility. Deep learning, a branch of machine learning that analyzes large datasets for pattern extraction, has demonstrated remarkable success in medical image processing.
This study proposes a multiclass deep learning framework that distinguishes between healthy and diseased skin while classifying various skin conditions. The model utilizes Convolutional Neural Networks (CNNs), a powerful algorithm for image classification capable of learning deep hierarchical features and achieving high accuracy. Trained on diverse dermatological datasets, the system effectively recognizes subtle visual variations among diseases, minimizing manual feature extraction.
The proposed model aims to advance dermatological diagnosis by integrating AI-driven methodologies, promoting early detection, improved treatment outcomes, and better access to quality healthcare.},
        keywords = {Skin disease, CNN, deep learning, classification, diagnosis, medical imaging, preprocessing},
        month = {May},
        }