Optimizing Melanoma Prognosis Through Synergistic Preprocessing and Deep Learning Architecture for Dermoscopic Thickness Prediction

Melanoma, the deadliest form of skin cancer, poses a significant threat to public health worldwide. The prognosis of melanoma is closely related to its thickness at the time of detection; thus, accurate and early prediction of tumor depth from dermoscopic images is crucial for patient survival. This paper introduces an innovative procedure for predicting melanoma thickness using advanced image processing techniques and deep learning algorithms. Our preprocessing pipeline includes novel techniques for skin lesion segmentation, image normalization and data augmentation, ensuring the model’s generalizability. We have employed a convolutional neural network (CNN) architecture tailored for feature extraction and thickness estimation, trained and validated on dermoscopic images. Our model outperformed existing methodologies, yielding an accuracy of 88.89% and an F1 score of 88.65%. These results signify a substantial advancement in non-invasive melanoma analysis, potentially leading to more timely and accurate clinical interventions.