CSDM-DEEP-CNN Based Skin Multi-function Disease Detection with Minimum Execution Time

Skin cancer is a prevalent and potentially fatal disease. Early detection is important for successful treatment. Traditional methods face challenges in identifying skin cancer regions. CSDM-Deep-CNN is a novel approach for efficient skin disease detection with minimal execution time. CSDM-Deep-CNN leverages deep convolutional neural networks with batch normalization. The objective of this study is to address the complexities in dermatology and the increasing impact of skin disorders on individuals’ psychological and social well-being. The proposed CSDM-Deep-CNN approach offers a promising solution by leveraging machine learning and deep learning technologies. The CSDM design and implementation involve pre-processing steps, image resizing, and the use of convolutional neural networks for disease prediction. The optimization process includes batch normalization to prevent overfitting, enhancing the training efficiency of the deep convolutional layer. The study reports promising results, including an accuracy rate of 84%, a training time of 1.59 s, and a total execution time of 4.23 s.