## 1 Introduction  
Machine learning (ML) has gained significant recognition within healthcare organizations as a potent tool for medical image analytics, particularly in the realm of disease diagnosis. More specifically, ML models can effectively extract meaningful insights from medical images, identify tumor occurrences, stage tumor progression, determine disease occurrences, predict future disease occurrences, and even recommend treatment procedures. Some of the commonly used ML algorithms include convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and support vector machines (SVMs).  
Among various medical image modalities, contrast-enhanced CT (CECT) is widely utilized for disease assessment since CECT offers several advantages over non-contrast CT. The advantages include improved visibility of blood vessels, better characterization of lesions, enhanced detection of tumors, improved assessment of organ perfusion, clearer delineation of anatomical structures, and detection of active inflammation. Some of the diseases that can be better diagnosed using CECT include oncological diseases, cardiovascular diseases, and gastrointestinal diseases.  
However, developing ML-based diagnosis systems with CECT presents two significant challenges: substantial technical complexity and a high development effort. Consequently, many of these systems suffer from limited diagnostic performance and are not accepted for use in medical clinics.  
The _high technical complexity_ of developing these systems is attributed to transforming the diagnosis processes applied by physicians into software processes. It is challenging mainly due to the deviations between the terms, practices, and standards used by physicians and those of software developers. Moreover, it is challenging to configure the most suitable ML algorithms for various diagnostic tasks, train them with an optimal set of hyperparameters, fine-tune trained ML models for optimal performance, manage control flows and data flows among the ML models, and synthesize various diagnostic results to determine ongoing diseases and their progression.  
The _high development effort_ is attributed to the considerable time required for designing the system architecture, functional components, data persistence, and runtime behavior. Additionally, the effort needed for designing, training, and tuning multiple ML models trained with different algorithms can be significantly high. This process also requires a team of skilled professionals, including data scientists, software engineers, and medical domain experts.  
Our strategy to address these challenges is to develop a machine learning-based software platform that captures the common diagnostic process with CECT, provides an essential set of functional components, manages data persistence, and supports a high level of configurability and extensibility for developing various CECT-based diagnostic systems. More specifically, we designed the platform by applying the Open-Closed Principle, where common features are designed to be fixed and variable features among diagnostic systems are designed to be customizable and extendable. The significant advantages of leveraging this platform for developing CECT-based diagnostic systems are the substantial reduction in technical complexity and the cost-effective development of high-quality diagnostic solutions.  
The paper is organized as follows: Section 2 summarizes related works on medical image analytics. Section 3 provides the fundamentals of medical analytics with CECT. Section 4 presents the design of the platform in terms of platform architecture, components, diagnostic process, database, and machine learning models. Section 5 elaborates on a case study of developing an HCC diagnosis system using the proposed platform.