Automated diagnosis of soft tissue tumors using Machine Learning

Diagnostic procedures concerning soft tissue tumors are an important issue in oncology, underlined by the need for correct, timely diagnosis that will buy time toward some leads on an effective treatment plan. Traditional diagnostic techniques, though reliable most of the time, are often invasive and time-consuming. Machine learning techniques are explored to establish the possibility of using them in the automated diagnosis of soft tissue tumors, therefore improving accuracy and reducing diagnosis time. We will make use of histopathological images and clinical data in a dataset, running a number of machine learning algorithms for the identification and classification of tumor types, including convolutional neural networks. Propose an improved approach to significantly improve diagnostic accuracy compared to the conventional methods, thus having the potential to assist pathologists in more informed decision-making. The potential integration of machine learning within diagnostic workflow is prone to revolutionize oncological principles by the offer of non-invasive, fast, and highly accurate alternatives for the early detection and classification of soft tissue tumors. Our results underline the feasibility and advantages that could be brought into reality by an automated diagnosis system and open ways toward developing more advanced, available tools in cancer diagnosis.