Automated Classification of Rare Cancers Using Deep Learning and Medical Imaging Data

Abstract: This study presents the development of an advanced deep learning–based system for the automated detection of synovial sarcoma from microscopic soft tissue images using Convolutional Neural Networks (CNNs). Synovial sarcoma is a rare and highly aggressive subtype of soft tissue sarcoma, where accurate and early diagnosis is essential for effective clinical management and improved patient prognosis. The proposed CNN framework is designed to automatically extract and learn discriminative features from histopathological images, enabling reliable identification of patterns characteristic of synovial sarcoma. By minimizing manual interpretation and inter-observer variability, the system serves as a supportive diagnostic tool for pathologists, enhancing diagnostic accuracy and efficiency. The results demonstrate the potential of deep learning techniques in improving histopathological analysis and contributing to timely and precise cancer diagnosis.