## 2 Related Works  
To produce predictions, the autoregressive moving average model and the grey prediction model were presented by [PERSON] et al. [7]. The generalized regression neural network was then used to dynamically weight based on the prediction results of two models. As a result, the neural network's prediction findings became more accurate, and provide a scientific foundation for the implementation of appropriate prevention and control actions. [PERSON] et al. [8] discussed that CXR images were first segmented using the random walker method, then computed features enabled the support vector machine (SVM) classifier in classifying CXR images as infected or healthy based on the computed features. The dataset, which was gathered from the Indiana University Hospital Network and consisted of 100 photographs, was used to assess the system performance. Using the SVM classifier, a system accuracy rate of 73% was calculated. [PERSON] et al. [9] explained some algorithms used to enhance images of CXRs to detect tuberculosis. These algorithms were evaluated by unsharp masking (UM), high-frequency emphasis filtering (HEF), and contrast limited adaptive histogram equalization (CLAHE). [PERSON] et al. used a deep learning (DL) approach for classification and area under curve (AUC) and achieved an 89.92% accuracy rate in classification and 94.80% in AUC. [PERSON] et al. [10] utilized a fuzzy method known as spatial fuzzy risk mapping to map the spatial risk of tuberculosis based on a variety of criteria that comprise TB risk factors. Among the criteria were the number of people with TB (BTM positive), population density, unsanitary living conditions, and health services. The weight value of each criterion was generated using fuzzy multi-criteria decision making, and the best alternative from the sub-district regions was chosen using a ranking method. Following the calculation of fuzzy membership, the sub-district areas were directly sorted into three index levels based on rule association: low, medium, and high. The TB risk index was calculated for 31 densely populated metropolitan areas in Surabaya, Indonesia. Using the risk map, a spatial geographic information system (GIS) mapping was created. There were four sub-districts that decreased (12.9%), six that expanded (19.4%), and the remaining 68.7% did not change over the last three years (2013-2015). In 2015, the fuzzy risk model classified 13.33% of sub-districts as low risk, while the Public Health Service classified people as high risk. [PERSON] et al. 's [11] proposal was tested on a large image dataset that was validated by three experts. Three CNN architectures were tested, each with three optimization strategies. The deeper design yielded better results, with accuracy levels above 99%. [PERSON] et al. [12] used an enhanced fuzzy C-means (FCM) clustering technique to pre-segment MRI images to track the approximate site of brain TB and provide the start contour for hybrid model segmentation. [PERSON] and [PERSON] [13] represented automatic TB detection method using conventional digital chest radiographs. There were three stages such as extracting using Gabor filtering, segmentation, and classification using support vector machine. [PERSON] et al. [14] suggested an efficient simple convolutional neural network for fast visualization of the chest X-ray images by the grad-CAM method. [PERSON] and [PERSON] [15] proposed modality-specific features from large-scale publicly available CXR collections by using a custom convolutional neural network (CNN). They proved some improved performance criteria using classification, accuracy, and AUC, which led to reduced overfitting and improved generalization. [PERSON] et al. [16] developed an automated approach to detect tuberculosis in conventional posteroanterior chest radiographs. Based on the graph cut, the lung region was segmented using an optimization method. [PERSON] et al.