Skin Insights: Advanced Analysis of Skin Condition using Hybrid Approach

Protecting our skin from growing pollution together with population growth and lifestyles, diagonalizing them earlier is important in our own wellness. In our investigation, we carry out a classification of skin diseases by testing a hybrid deep learning technique. The method proposed consists of a synergistic effect of convolutional neural networks (CNN), generative adversarial networks (GANs), and long short-term memory (LSTM) networks. First, CNN works for proper feature extraction from dermoscopic images, then GAN for enhancing data quality and robustness of the models. Then LSTM further develops the system, depending on the presence of sequential patterns that contribute to better diagnostic context and temporal precision. The model is trained and tested on a large data set of skin disease images and achieves an accuracy of 96% in classification. The results indicate that these models associate themselves with high-resolution medical imagery and the complexities of varied skin conditions. Therefore, the system shows much potential to provide a dermatologist with fast, accurate, and custom diagnostic support. Also, the collaboration with medical experts offers assurance that the model development steps are in line with clinical practice for future medical use.