Implementation of InceptionV3 Model for Skin Disease Detection and Classification

Skin disorders represent a significant worldwide health issue, and timely diagnosis and classification are necessary for effective treatment. In this work, we present an improved approach for the identification and recognition of skin illnesses using a DL based system. Using the powerful Inception V3 convolutional neural network architecture, we propose a new method. The novel work specifies in the diagnosis and classification of a broad variety of skin disorders, counting normal skin as well as rashes, melanoma, keloid, measles, chickenpox, monkeypox, and basal cell carcinoma. The Inception V3 model is trained using a dataset that contains annotated samples of diverse skin conditions. Images of skin lesions are then preprocessed to add more features. The pre-trained Inception V3 network is transfer learning-tuned to our specific task, enabling it to learn the discriminative characteristics required for skin condition identification. During the training phase, the algorithm learned to extract complicated patterns and information from photographs of skin lesions, which allows it to identify between various skin illnesses with excellent accuracy. We employ techniques like proposed data augmentation to expand the representation capability to simplify while lowering overfitting. Various test datasets are utilized for validation in direction to assess the trained representation accuracy in real-world scenarios. Our tests' results demonstrate how effectively the recommended approach may accurately diagnose and classify a range of skin problems. Among the notable performance measures the algorithm achieves across a range of classes are high accuracy, sensitivity, and specificity. The model is shown to have superior performance and shows potential for clinical applications through cross-validation and comparison with existing methods to evaluate its resilience.