Enhancing Skin Cancer Prediction: A Hybrid RESNETLSTM and GAN Approach

Skin cancer is a serious public health concern, demanding accurate and timely detection in order to improve patient outcomes. This study introduces a sophisticated hybrid model that combines RESNET-LSTM and Generative Adversarial Networks (GAN) to improve skin cancer prediction across a variety of lesions. The suggested method makes use of RESNET's feature extraction capabilities, as well as LSTM layers, to discover sequential patterns in dermatoscopic pictures, allowing for a more extensive examination of lesion attributes. GANs are also used to generate synthetic data, which expands the dataset with a variety of images to eliminate bias and improve the model's adaptivity.Using the huge HAM10000 dataset for training, our model achieves a high classification accuracy of 94.10% across seven unique skin cancer categories: benign, malignant, actinic keratosis, basal cell carcinoma, dermatofibroma, melanocytic nevus, and vascular lesion. The combination of RESNET-LSTM and GAN addresses important issues in dermatological imaging, such as low data diversity and the complexities of collecting spatial and temporal patterns within skin diseases. Our findings demonstrate the efficiency of this hybrid technique in distinguishing between benign and malignant lesions, allowing dermatologists to diagnose skin cancer earlier and more reliably. The outstanding performance demonstrated across various disease categories highlights the AIdriven method's potential for clinical applications, boosting the accuracy and range of automated skin cancer detection.