# Precision Diagnosis of Glaucoma with VLLM Ensemble Deep Learning
## 3 Proposed Method
### ROI and VROI  
Figure 3 depicts a modified attention U-Net, ROI, and VROI that have been successfully extracted. This enhanced version of U-Net leverages attention mechanisms to focus more precisely on the relevant features within the images, such as specific anatomical structures in medical imaging. The attention module within the network helps to improve the accuracy of segmenting and highlighting critical areas, which is particularly useful for tasks requiring detailed analysis of specific regions within larger images. This approach not only improves the quality of the segmentation but also increases the efficiency of the analysis by directing computational resources toward areas of the image that contain the most pertinent information. In retina fundus images, extracting just the cup-disk region, referred to as the cup-disk ROI image, is crucial, as it intuitively reveals structural changes and provides key indicators for diagnosing glaucoma. Typically, if the vertical measurement of the CDR exceeds 0.5, it is indicative of glaucoma. Accurately cropping this small area from fundus images to use as input data is essential. We have employed a modified attention U-net to extract this ROI from the retina fundus images. The input data are resized to 256 \(\times\) 256 and then passed through a convolution network to extract features. After passing through two convolutions (resulting in a 128 \(\times\) 128 feature map), the pooling process reduces this to a 64 \(\times\) 64 feature map, which is then concatenated in the next layer. This acts as an encoder that captures the features of the input image. Following this, the decoding process is used to identify regions of interest (ROI) and  
Figure 2: When applied with various color palettes, the heat vision cameras from ATN Corp show different recognition capabilities. Further details can be found in [26]. produce the final result. At this stage, mask data indicating the regions of interest in the input data is required. The decoding results are compared with the mask, training the network to ensure that the identified ROI matches the mask regions. The attention gate plays a crucial role by assigning weights to the regions of interest, allowing the network to focus more on important information (the ROIs). The attention gate takes the gate signal (\(g\)) and the feature map (\(x_{i}\)), which are passed through a skip connection as inputs. Since \(g\) is a 32 \(\times\) 32 image, it undergoes transpose convolution to become 64 \(\times\) 64, matching the size of \(x_{i}\). The two vectors are then element-wise added, where the central weights become larger and the noncentral weights become relatively smaller. This result is passed through ReLU and then through Sigmoid to create the attention coefficients. A higher attention coefficient indicates a more important area to focus on. In this process, we utilize the mask data to refine the attention coefficients by calculating the weights for the pixel positions in the ROI, creating a modified attention gate. The resulting attention coefficients are then multiplied with the previous feature map, scaling according to the object relevance and further enhancing the learning process. The eye is the only part of the body where blood vessels can be visibly analyzed, and they play a crucial role in carrying and distributing blood. Problems in blood circulation can lead to blocked and progressively thickening vessels, eventually causing new capillaries to form around the thickened areas. Persistent issues may cause these thickened vessels to burst, leading to hemorrhage. Such characteristics are indicative of ophthalmic diseases. In cases of glaucoma, damage to the optic nerve bundles prevents the visual information received by the eyes from being transmitted to the brain, leading to blindness. As the optic  
Figure 3: Modified Attention U-Net. The encoder part extracts features, and the decoder part uses these extracted features to generate images for specific purposes (ROI and VROI).