Article
Version 1
This version is not peer-reviewed
Machine Learning Platform for Disease Diagnosiswith Contrast CT Scans
Version 1
: Received: 25 July 2024 / Approved: 29 July 2024 / Online: 29 July 2024 (10:02:01 CEST)
How to cite: Jin, J.; Kim, M.; Kim, S. D.; Jin, D. Machine Learning Platform for Disease Diagnosiswith Contrast CT Scans. Preprints 2024, 2024072284. https://doi.org/10.20944/preprints202407.2284.v1 Jin, J.; Kim, M.; Kim, S. D.; Jin, D. Machine Learning Platform for Disease Diagnosiswith Contrast CT Scans. Preprints 2024, 2024072284. https://doi.org/10.20944/preprints202407.2284.v1
Abstract
Machine learning has gained significant recognition within healthcare organizations as a potent technology for medical image analytics. Among various medical image modalities, Contrast-Enhanced CT (CECT) is widely utilized to acquire additional diagnostic information that can help to better visualize and evaluate certain structures or abnormalities in the human body. Accordingly, there is an increasing demand for machine learning systems for disease analytics using CECT. However, developing such systems presents two significant challenges: high technical complexity and high development effort. This paper presents a software platform that can effectively remedy these challenges. This platform embodies a unified software process for CECT-specific disease diagnosis including oncological diseases, cardiovascular diseases, and gastrointestinal diseases. It integrates multiple types of machine learning models into the disease diagnosis process, where the models can diagnose lesions, malignancies, tumors, medical features of tumors, temporal transitions with a contrast agent, and disease progress. The platform has been implemented in Python using Scikit-learn and TensorFlow libraries. To validate its applicability and reusability, a Hepatocellular Carcinoma (HCC) diagnosis system has been implemented.
Keywords
machine learning platform; medical image analytics; contrast-enhanced ct; open closed principle (OCP); liver cancer diagnosis
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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