A Novel Predictive Analysis of Skin Disease Diagnosis for Medical Image Processing Using Optimized Deep Learning Techniques

Perhaps there are numerous skin illnesses that afflict humans, which can be predicted and diagnosed pathologically. This research proposes combining two powerful deep learning approaches. The system extracts feature from medical scan images based on skin biopsy of skin conditions to predict the disease using an optimize deep learning techniques such as ResNet-50 and Convolutional Neural Network (CNN) renowned for its residual learning capabilities. Because of its depth, the network can extract features with great accuracy, which make it ideal for intricate image analysis. Following their extraction, the features are run through a LSTM (Long Short-Term Memory) network, which improves prediction accuracy by spotting sequential patterns in the multimodal data. The diagnosis accuracy of the model is further improved by the LSTM’s exceptional ability to handle textual descriptions of symptoms or patient history. The novel hybrid model to integrate CNN with LSTM to predict high accuracy result produced in this model. The accuracy and efficiency of ResNet-50 is 87.7%, CNN is 91.89% and the hybrid techniques of CNN+LSTM is 97.61%.