Automated system for classification of COVID-19 infection from lung CT images based on machine learning and deep learning techniques.

ABSTRACT: The objectives of our proposed study were as follows: First objective is to segment the CT images using a k-means clustering algorithm for extracting the region of interest and to extract textural features using gray level co-occurrence matrix (GLCM). Second objective is to implement machine learning classifiers such as Naïve bayes, bagging and Reptree to classify the images into two image classes namely COVID and non-COVID and to compare the performance of the three pre-trained CNN models such as AlexNet, ResNet50 and SqueezeNet with that of the proposed machine learning classifiers. Our dataset consists of 100 COVID and non-COVID images which are pre-processed and segmented with our proposed algorithm. Following the feature extraction process, three machine learning classifiers (Naive Ba

SUBMITTER: Guhan B 

PROVIDER: S-EPMC9579174 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature