Enhanced Skin Disease Classification Using Modified ResNet50 Architecture

Skin disorders are a major global health problem that impact millions of people and provide formidable obstacles to healthcare systems. Deep learning-based automated categorization of skin diseases presents potential options for precise and effective diagnosis, allowing for prompt interventions and individualized therapies. We provide a thorough investigation into the categorization of skin diseases with a modified ResNet50 convolutional neural network (CNN) in this publication. The suggested model makes use of deep learning's built-in capabilities to precisely identify different skin illnesses based on how they seem visually by extracting detailed information from photos of skin lesions. The modified ResNet50 architecture incorporates additional fully connected layers and dropout regularization to enhance feature extraction and prevent overfitting, while residual connections facilitate efficient information flow within the network. The model is trained and evaluated using a diverse dataset comprising images of different skin diseases categorized into eight classes, including bacterial, fungal, parasitic infections, and viral infections. Rigorous data preprocessing techniques, model configuration, and hyperparameter tuning are employed to ensure optimal model performance and generalization capabilities. The suggested strategy outperforms current approaches in obtaining high accuracy and resilience in the categorization of skin diseases, according to extensive experimental data. The developed model holds significant potential for real-world applications in dermatology clinics, offering clinicians an efficient tool for rapid and accurate skin disease diagnosis. Furthermore, the insights gained from this study contribute to advancing the field of computer-aided dermatology and pave the way for future research in automated skin disease diagnosis and management.