Segmentation of Skin Lesions from Medical Images Using a Fusion of CNN and RNN Models

The necessity for accurate detection of skin cancer has led to very significant advances in medical image analysis, mainly through the application of deep learning models. Detection of early skin lesions such as basal cell carcinoma, squamous cell carcinoma, keratoacanthoma, and melanoma is highly critical for improving patient outcomes and treatment efficiency. A combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) is used in this research to improve the classification and prediction of skin lesions. CNN models VGGI6, VGGI9, and ResNet50 are applied to spatial features in skin lesion images, while RNNs manage the handling of temporal data such as lesion growth, treatment responses, and follow-up records. The models are trained to differentiate between various skin cancers and predict disease progression. The performance evaluations prove that the VGG 19 + RNN model presented the highest accuracy at a level of 96.76%, having both precision and recall, proving that the performance is also strong at the F1-score. VGG16 + RNN and ResNet50 + RNN followed in order, with slightly lower performances, which demonstrate the potential for CNN-RNN fusion to tackle complex diagnostic tasks. The VGG 19 + RNN model's excellent results make it the preferable candidate for real-time clinical applications since it can function with the capabilities of both image and temporal data. It is thus a good source for automatically diagnosed skin cancer, providing efficient, reliable means to healthcare professionals for the early detection and monitoring of treatment. This study highlights the potential that deep learning models can have in transforming medical diagnostics, offering a scale-up solution for improved detection of skin cancer and patient care.