## 5 Practical Application  
Artificial intelligence (AI) and machine learning (ML)-based diagnostics have been widely utilized by researchers and practitioners to detect risks and facilitate decision-making in a range of contexts, from the prediction of chronic diseases like cancer [70; 71] to psychological disorders [72]. Therefore, this ML-based deep learning model yield \begin{table}
\begin{tabular}{c c c c} \hline
**Reference** & **Accuracy (\%)** & **AUC (\%)** & **Notes** \\ \hline
[64] & 88.86 & 83.59 & Experiment 1: using CNN pre-trained model \\ \hline
[64] & 85.26 & 80 & Experiment 2: using CNN+SVM \\ \hline
[65] & - & 86.8 & Experiment 1: using a random training technique \\ \hline
[65] & - & 87.4 & Experiment 2: using cross-validation \\ \hline
[66] & - & 83.1 & - \\ \hline
[67] & - & 83.8 & - \\ \hline
[68] & - & 83.8 & - \\ \hline
[69] & - & 82.3 & - \\ \hline
[32] & - & 85.1 & - \\ \hline
[40] & - & 84.83 & - \\ \hline
[41] & 91.2 & - & - \\ \hline
**Our proposed method (2022)** & **92.88** & **89.34** & **Using DenseNet** \\ \hline \end{tabular}
\end{table} 
Table 5: Comparison results with findings from earlier works. in this experiment can be further implemented in web/mobile-based applications for practical usage and gives maximum benefits of early glaucoma detection to the worldwide community. Hence, the further objective of this work is to design and implement an early glaucoma detection platform. This platform then can be implemented as Software as a Service (SaaS) to facilitate medical teams' decision-making in glaucoma screening. In this research, we demonstrated the practicability of our developed model using the TensorFlow framework implemented using Python programming language. Python has a feature which allows the trained model to be saved on the specified so-called HDF5 filesystem. HDF5 (Hierarchical Data Format version 5) is an open-source file format that supports large, complex, and heterogeneous data, which is perfectly suited to save the distributed representation of the DL model and its weights. Since Python seamlessly supports HDF5, we then used Python-based architecture as the main ingredient for the glaucoma detection platform and provided API (Application Programming Interface) services for this purpose. Figure 14 illustrates the workflow and architecture of the proposed system design. In the architecture depicted in Figure 15, an application can be accessed by the user (the medical team) either through their personal computer (PC) and a web browser, or through their mobile device and an application that is connected to the fundus camera. The proposed architecture required a fundus camera as the input device. A fundus camera is a specified device used for fundus photography. The back of the eye, also called the fundus, is the subject of photography in the field of fundus photography. In fundus photography, the usage of specialized fundus cameras that consist of a complex microscope attached to a camera with a flash capability is required [73]. The central and peripheral retina, optic disc, and macula are the primary components that can be seen in a fundus photograph. Other structures, such as the choroid, may also be visible. Photography of the fundus can be done using colored filters or with specific dyes like fluorescein and indocyanine green [74]. Currently, many types of fundus cameras with various specifications are widely available on the market. Figure 16 shows a picture of a fundus camera that is typically used in a healthcare facility. Figure 16: Examples of fundus cameras used in healthcare facilities [75]. Figure 15: The architecture and workflow of early glaucoma detection platform. The fundus camera serves as an input device that scans the eyes in order to provide an accurate image of the eye. That image is then transmitted together with the feature data. After that, the input feature data are sent to a web server, where our proposed model is utilized to determine whether or not the subjects in question have glaucoma.