# Classification of Valvular Regurgitation Using Echocardiography
###### Abstract
Regurgitation can make it difficult for a person to breathe because of the blood's improper inflow and outflow, which is a concern. A cardiologist can determine whether a person is healthy or has any regurgitation or disorders using visualization and knowledge which needs precise identification of many images before accurately predicting such abnormalities. As a result, if regurgitation can be detected early, proper diagnosis and treatment can be provided. This can be accomplished with the use of automated tools and approaches. The usage of automated tools is critical since it reduces human effort, reduces the necessity of invasive procedures and allows more precise prediction of various problems in the heart. It has become prominent in medical imaging due to its accessibility, efficiency and effectiveness. Different techniques can achieve automatic disease diagnosis, such as traditional methods, machine learning, deep learning, and reinforcement learning. Significant research and analysis are required before applying such tools in medical imaging. Many researchers are trying to find a suitable application that will make human intervention easier and possibly obsolete. To date, no such application has been established which is applicable in clinics and hospitals. Work in classification and prediction of the type of regurgitation employing automated technologies is ongoing, and traditional approaches or advanced state-of-art techniques are being used. Here, work is done for classification using a simpler model involving preprocessing, keyframe extraction, segmentation, feature extraction, and classification. This step-by  
Figure 1: Diagram showing Doppler echo from the dataset of patients having (**a**) aortic regurgitation (AR), (**b**) mitral regurgitation (MR), and (**c**) tricuspid regurgitation (TR), respectively. step procedure is significant for a detailed analysis and prognosis of heart abnormalities as a whole. These are the main steps in automated diagnosis. In addition, keyframe extraction has been included in this work. The pictorial representation of the steps involved is shown in Figure 2. Preprocessing is a phase in image processing that helps to reduce the presence of noise and other undesirable data and artifacts in an image. Preprocessing allows for greater clarity and understanding of the image, resulting in a more effective result. There are many techniques for preprocessing, some of which are filtering techniques, morphological operations, and statistical techniques [11; 12]. Keyframe extraction is a crucial step in video analysis which is efficient in indexing [13]. It is usually applied in movies, sports videos, low-quality videos, etc. [14]. It is a step used to extract keyframes to suffice comprehensive analysis of videos by eliminating replication. It is also sometimes used to reduce the number of frames instead of using all of them. Some techniques used are clustering techniques, color-based techniques, hash function, global comparison, motion-based, and others [13; 14; 15]. Segmentation is one of the most important aspects in diagnosis of disease for detecting a region or regions where abnormalities are located. Techniques commonly used in segmentation are threshold based techniques, clustering techniques, statistical methods [12], and other methods such as deep learning. A ground truth image is required when employing deep learning methodology, which can be a disadvantage in some circumstances where one is not available. Feature extraction is a step to extract useful information based on properties such as texture, shape, and color. This process is essential in classifying images as it provides a more meaningful representation of the image. A specific region can be localized or globalized according to a particular application [12]. Classification is a step that classifies the images or a region into different classes. Classes can be predefined (supervised) or random (unsupervised) [5]. It usually involves data division into training (training phase) and testing (testing phase) data.