Aiding the Diagnosis of Diabetic and Hypertensive Retinopathy Using Artificial Intelligence-Based Semantic Segmentation

Abstract

Automatic segmentation of retinal images is an important task in computer-assisted medical image analysis for the diagnosis of diseases such as hypertension, diabetic and hypertensive retinopathy, and arteriosclerosis. Among the diseases, diabetic retinopathy, which is the leading cause of vision detachment, can be diagnosed early through the detection of retinal vessels. The manual detection of these retinal vessels is a time-consuming process that can be automated with the help of artificial intelligence with deep learning. The detection of vessels is difficult due to intensity variation and noise from non-ideal imaging. Although there are deep learning approaches for vessel segmentation, these methods require many trainable parameters, which increase the network complexity. To address these issues, this paper presents a dual-residual-stream-based vessel segmentation network (Vess-Net), which is not as deep as conventional semantic segmentation networks, but provides good segmentation with few trainable parameters and layers. The method takes advantage of artificial intelligence for semantic segmentation to aid the diagnosis of retinopathy. To evaluate the proposed Vess-Net method, experiments were conducted with three publicly available datasets for vessel segmentation: digital retinal images for vessel extraction (DRIVE), the Child Heart Health Study in England (CHASE-DB1), and structured analysis of retina (STARE). Experimental results show that Vess-Net achieved superior performance for all datasets with sensitivity (Se), specificity (Sp), area under the curve (AUC), and accuracy (Acc) of 80.22%, 98.1%, 98.2%, and 96.55% for DRVIE; 82.06%, 98.41%, 98.0%, and 97.26% for CHASE-DB1; and 85.26%, 97.91%, 98.83%, and 96.97% for STARE dataset.

Artificial Intelligence-Based Diagnosis of Cardiac and Related Diseases

“…Unlike other traditional networks where the final feature map is small (7 × 7) [51], the X-RayNet maintains the final feature map at 21 × 21 for a 350 × 350 CXR image with a total of 17 layers overall. Table 2 lists the key differences of the proposed X-RayNet with deep networks, such as ResNet [52], SegNet [53], IrisDenseNet [54], fully residual encoder-decoder network (FRED-Net) [55], outer residual skip network (OR-Skip-Net) [56], Vess-Net [15], and U-Net [57], in different application domains. Considering the mesh residual structure of X-RayNet, Figure 2 shows the layer connectivity of the candidate encoder and decoder block with a feature empowerment scheme.…”

“…Automatic pulmonary disease detection using computer-aided diagnosis (CAD) is based on the correct segmentation of anatomical structures, such as the lungs, heart, and clavicle bones [2]. With the success of deep learning, artificially intelligent algorithms can help medical experts and ophthalmologists to detect and diagnose the disease and increase diagnostic throughput [14][15][16][17][18][19][20]. Semantic segmentation is a special branch of deep learning that involves pixel-wise classification of the image, which is important to accurately locate the infected areas for disease analysis [21,22].…”

Artificial Intelligence-Based Mitosis Detection in Breast Cancer Histopathology Images Using Faster R-CNN and Deep CNNs

“…A false positive means that the ground truth non-mitotic cell is incorrectly detected as a mitotic cell. Based on these, precision, recall, and F1-measure are used for the evaluation, as shown in Equations (7)- (9).…”

“…F1 − measure = 2 Precision Recall Precision + Recall (9) where TP is the number of true positives, FP is the number of false positives, and FN is the number of false negatives. Tables 2 and 3 present the comparative accuracies of the proposed method with the state-of-the-art methods used with the ICPR 2012 and ICPR 2014 datasets.…”

“…The majority of the processes are now automated and can even be used as a second-opinion system in medical diagnosis. AI techniques [3][4][5][6][7][8][9][10] have been developed previously for solving problems in the medical field. Mitotic-cell detection can also be automated using AI techniques; however, it comprises several challenges.…”