Designing A Cost-Effective And Affordable Iot Based Device For Skin Disease Identification
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Abstract
The skin is the body's largest organ and covers the entire external surface of the body. It is composed of three layers, namely the epidermis, dermis, and subcutaneous tissue, and all three differ greatly in anatomy and function. Skin conditions can range widely in severity and presentation, and successful treatment frequently depends on a timely and precise diagnosis. In this work, we provide a deep learning-based algorithm-based complete strategy for the detection and categorization of skin diseases. We fine-tuned the Inception V3 convolutional neural network architecture to recognize a range of skin disorders, including normal skin, rashes, monkeypox, melanoma, keloid, and basal cell carcinoma. This is accomplished by utilizing the architecture's pre-training on the extensive Image Net dataset. When a skin illness is identified and categorized, the system sends out a medical warning, giving medical personnel enough time to take appropriate action. Moreover, the patient's skin disease detection status is updated in real-time through LCD display integration in order to guarantee effective communication and monitoring.
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