Research Article
GLCM Based Feature Extraction and Medical X-ray Image Classification Using Machine Learning Techniques
@INPROCEEDINGS{10.1007/978-3-031-35078-8_6, author={Jyotiranjan Rout and Swagat Kumar Das and Priyabrata Mohalik and Subhashree Mohanty and Chandan Kumar Mohanty and Susil Kumar Behera}, title={GLCM Based Feature Extraction and Medical X-ray Image Classification Using Machine Learning Techniques}, proceedings={Intelligent Systems and Machine Learning. First EAI International Conference, ICISML 2022, Hyderabad, India, December 16-17, 2022, Proceedings, Part I}, proceedings_a={ICISML}, year={2023}, month={7}, keywords={GLCM Machine Learning random forest SMOTE}, doi={10.1007/978-3-031-35078-8_6} }- Jyotiranjan Rout
Swagat Kumar Das
Priyabrata Mohalik
Subhashree Mohanty
Chandan Kumar Mohanty
Susil Kumar Behera
Year: 2023
GLCM Based Feature Extraction and Medical X-ray Image Classification Using Machine Learning Techniques
ICISML
Springer
DOI: 10.1007/978-3-031-35078-8_6
Abstract
The automated system is now created with excellent accuracy to detect abnormalities in X-ray images. To enhance the appearance of medical photographs, image pre-processing methods are applied, so that high accuracy can be achieved with constrained means. Images are often classified based on their textural properties, which are measured using the Gray Level Co-occurrence Matrix (GLCM). The grey level correlation matrix provides statistical information of the second order on the grey levels of neighboring pixels in a picture (GLCM). In this proposed paper, medical X-ray images are classified and their features are extracted using an ensemble learning model. By extracting image features using the GLCM feature extraction method, this proposed model is able to distinguish between healthy and sick images (Gray level co-occurrence matrix).to improve the efficiency of the Ensemble learning classification method, it is compared against various algorithms using performance indicators, including Logistic regression, Gaussian Naive Bayes, as well as Random Forest. When this approach is compared to existing methods, the proposed ensemble model has an accuracy rate of 97% in classifying normal and diseased images.
