## 6 Assessment of the Platform
### Evaluating Functional Coverage of the Platform  
The _functional coverage_ of software platforms refers to the extent to which the platform's features and capabilities meet the various requirements of potential applications that can be built with the platform [37]. It ensures that all necessary functionalities are implemented and can handle the diverse needs of medical diagnostics using CECT scans.  
We define a metric, _Functional Coverage (FC)_, for the functional coverage of the platform as follows:  
\[FC=\frac{N_{f}}{N_{t}}\]  
\(N_{f}\) is the number of features implemented by the platform, and \(N_{t}\) is the total number of features required by the family of medical diagnostic applications using CECT scans. The value range of \(CR\) is from 0 to 1, where 0 indicates that none of the required features have been implemented and 1 indicates that all required features have been fully implemented.  
The value of \(N_{f}\) is computed as the sum of the four coverage-relevant categories:  
* The number of functional components that fulfill the functional requirements.
* The number of persistent object classes that manage the persistent datasets.
* The number of diagnostic tasks specified in the main diagnosis process.
* The number of machine learning models required by the functional components.  
The functional components of the platform were derived directly from the use-case diagram of the platform. Hence, the set of 12 functional components effectively fulfills the requirements.  
The persistent object classes were derived from the data manipulated by each functional component. Hence, the set of 16 persistent object classes represents the persistent datasets managed by medical image analytics systems.  
The 8 steps, 3 through 10, of the defined diagnostic process are directly mapped to the diagnosis tasks for diagnosing the target diseases with the CECT, as defined in various standards such as LI-RADS and LUNG-RADS.  
The machine learning models needed for diagnosing diseases were derived by considering the intrinsic nature of the required diagnostic tasks. A total of six machine learning models, trained with CNN, RNN, and SVM classification algorithms, sufficiently assist with the required diagnostic tasks.  
Accordingly, the \(N_{f}\) for the platform is computed as 42, as shown in Table 5.  
All 42 features of the platform have been applicable to developing the liver cancer diagnosis system with the platform. In addition, the liver cancer diagnosis system required additional features to fulfill the diagnosis process and guidelines of the LI-RADS.  
* Additional Functional Component: _Liver Lesion Tracker_ This is a specialized software component designed to monitor and document changes in liver lesions over time. This functionality is crucial for the effective management and treatment of liver diseases, particularly hepatocellular carcinoma (HCC), as specified in the LI-RADS framework.
* Additional Task in the Diagnosis Process: _Specializing Step 9 for Tracking Lesions_ Step 9 of the diagnosis process in Section 4.4 has been specialized to support the additional functionality of tracking lesions on the liver.
* An Additional ML Model in the Diagnosis Process: _Hepatic Segmentation Model_ This model is required for segmenting hepatic segments on liver organs. These models are trained using the U-Net algorithm to utilize the encoder-decoder feature that allows for precise localization and segmentation.  
The numbers of these additional features required for the liver cancer diagnosis system are shown in the third column of the table. The value of \(N_{t}\), the number of features required for this target system, becomes 45. This will yield 93.3% of the _Functional Coverage (FC)_ for the case study of constructing the LI-RADS application.