Harnessing CRNN for Robust and Efficient Skin Cancer Detection in Medical Imaging

Skin cancer (SC) ranks as one of the most prevalent and deadly forms of cancer, with a significant rise in instances globally. If it is undiagnosed in the early stages, it may result in metastasis, leading to elevated mortality rates. SC is curable if identified promptly. In recent years, deep learning (DL) algorithms have become formidable instruments for interpreting medical imaging data and aiding clinicians in the diagnosis of skin cancer. The growing accessibility of dermoscopic pictures and progress in deep learning have rendered automated systems a valuable asset for aiding in skin cancer identification. This study presents a robust skin cancer classification model utilizing Convolutional Recurrent Neural Networks (CRNN) to improve diagnostic accuracy and reliability. It combines the best features of Convolutional Neural Networks (CNNs) with the best features of Recurrent Neural Networks (RNNs), especially Long Short-Term Memory (LSTM) units, for learning from context. We utilize CNN layers to extract spatial features from dermoscopic pictures of skin lesions, while LSTM facilitates the learning of intricate correlations between feature maps, thereby enhancing the model's ability to distinguish between melanoma and non-melanoma skin malignancies. For the CRNN model to be more general, it uses the ISIC dataset and advanced data enhancement methods to make it work better with different skin tones, types of lesions, and imaging conditions. Performance is assessed by metrics including accuracy, sensitivity, and specificity, emphasizing the reduction of false negatives to guarantee dependable early detection. The results indicate that the proposed CRNN model much surpasses conventional CNN architectures, establishing it as a reliable instrument for real-time clinical applications in dermatology.