Advanced Diagnosing and Localizing Melanoma from Whole-Slide Images with Convolutional Neural Networks

Abstract: In this work, a sophisticated deep learning method for melanoma diagnosis and localization using whole-slide histopathology pictures is presented. The suggested technique efficiently extracts and evaluates high-dimensional information from large-scale slide pictures by the use of convolutional neural networks (CNNs), which enable accurate detection of the melanoma region. To manage the enormous size and complexity of whole-slide images, the system combines preprocessing methods, patch-wise analysis, and aggregation strategies. CNNs have the potential to improve digital pathology processes and assist clinical decision-making in dermatology, as evidenced by experimental data showing greater performance over conventional approaches in terms of diagnostic accuracy and lesion location.

Keywords: feature extraction, image pre-processing, lesion localization, medical image analysis, whole-slide images (WSIs), convolutional neural networks (CNNs), and melanoma diagnosis