  The popularity of Artificial intelligence and machine learning have prompted
researchers to use it in the recent researches. The proposed method uses
K-Nearest Neighbor (KNN) algorithm for segmentation of medical images,
extracting of image features for analysis by classifying the data based on the
neural networks. Classification of the images in medical imaging is very
important, KNN is one suitable algorithm which is simple, conceptual and
computational, which provides very good accuracy in results. KNN algorithm is a
unique user-friendly approach with wide range of applications in machine
learning algorithms which are majorly used for the various image processing
applications including classification, segmentation and regression issues of
the image processing. The proposed system uses gray level co-occurrence matrix
features. The trained neural network has been tested successfully on a group of
echocardiographic images, errors were compared using regression plot. The
results of the algorithm are tested using various quantitative as well as
qualitative metrics and proven to exhibit better performance in terms of both
quantitative and qualitative metrics in terms of current state-of-the-art
methods in the related area. To compare the performance of trained neural
network the regression analysis performed showed a good correlation.
