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.

翻译：人工智能与机器学习的普及推动了研究人员在近年研究中的应用。本文提出一种基于K-最近邻（KNN）算法的医学图像分割方法，通过神经网络对数据进行分类以提取图像特征进行分析。医学图像分类至关重要，KNN作为一种简洁、概念清晰且计算简单的算法，在结果中提供了极好的准确率。KNN算法是一种独特的用户友好型方法，广泛应用于机器学习算法中，这些算法主要用于包括分类、分割和回归问题的各类图像处理任务。本系统采用灰度共生矩阵特征。训练后的神经网络已成功在一组心超声图像上进行测试，并通过回归图进行误差比较。算法结果采用多种定量与定性指标进行验证，证明其在相关领域的最新方法中，在定量和定性指标方面均表现出更优性能。为比较训练神经网络的性能，进行的回归分析显示出良好的相关性。