## 7 Concluding Remarks  
In this paper, we have introduced a comprehensive machine learning-based software platform designed to address the complexities and challenges associated with diagnosing diseases using CECT scans. The platform provides a unified software process that can fully automate the diagnosis practices of various diseases using CECT scans. In addition, the platform effectively integrates a suite of specialized machine learning models, including CNN models for segmenting organs, lesions, and tumors, RNN models for observing temporal changes in tumors, and SVM models for disease classification and staging.  
As the key software strategy, we leveraged the Open-Closed Principle (OCP) to develop a highly configurable and extendable system architecture, ensuring that common features remain stable while allowing for customization to meet the specific needs of various diagnostic applications. The platform's architecture, based on the microkernelstyle, separates core control functionalities from plugin control functionalities, enhancing modularity and scalability.  
Through a detailed case study, we demonstrated the platform's applicability by developing a liver cancer diagnosis system that adheres to the LI-RADS standard. The case study highlighted the platform's ability to support the end-to-end diagnostic process, from CT scan acquisition to generating comprehensive diagnosis reports.  
We evaluated the proposed platform using two evaluation metrics: Functional Coverage (FC) and Feature Satisfaction (FS). Each of these evaluation metrics was applied to the case study of developing a liver cancer diagnosis system. Our platform achieved an overall FC score of 93.3% and an overall FS score of 91.4%, indicating its robustness and effectiveness in the medical application domain.  
The key contributions of our research can be summarized in the following three points:  
* _Unified Software Process of Diagnosis_: Proposing a software process that fully automates the entire procedure involved in CECT-based disease diagnosis.
* _Integration of Essential ML Models_: Identifying and integrating six core machine learning models to support various diagnostic tasks, thereby reducing the technical complexity of developing high-performing diagnostic systems.
* _Configurable and Extensible Design_: Designing the platform to be highly configurable and extendable, enabling efficient customization for different diagnostic requirements.  
Our future work should focus on expanding the types of diseases that can be diagnosed using our platform, integrating more dynamic and self-learning algorithms, and enhancing the platform's ability to manage and analyze large datasets from diverse populations. We note that conducting a case study of developing an application with the proposed platform requires a tremendous amount of time and effort. This is primarily due to the required efforts for acquiring a sufficient number of CECT scans, labeling the CT slices for training purposes, training the required ML models, and fine-tuning the model for acceptable performances. By cooperating with university hospitals and private clinics, we aim to conduct each case study within approximately six months.  
[PERSON] and [PERSON] conceptualized the research scope and coordinated the co-authoring of the paper and peer review. [PERSON] designed the software platform and led the implementation of the platform and the case study system. [PERSON] was responsible for governing the software process and methods that automate the application of LI-RADS. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author due to privacy.  
The authors declare no conflicts of interest.