Get started
Work

Local Mean Gradient Pattern (LMGP): A Novel Approach for theClassification of Brain CT Scan Images

Year: 2025
Type: article
Abstract: ABSTRACT Objective The visual descriptor methods like Local Binary Pattern (LBP) capture anatomical structures in captured images along with their disparities, which can be exploited by suitable methods for the diagnosis of medical anomalies. We developed a Local Mean Gradient Pattern (LMGP), based partly on LBP, a feature extraction algorithm for the classification of Computed Tomography (CT) images of the brain into normal, ischemic, or hemorrhage categories. Methods The ASID dataset comprises of 397 acute ischemic stroke CT scan images and the Kaggle dataset consists of 200 CT scan images, out of which, 100 images were of the abnormal brain type (hemorrhage), and the remaining 100 images which don’t show any abnormalities were categorized as normal cases. The results of LMGP were compared with eight other feature extraction methods using linear, sigmoid, polynomial, and RBF kernels of the SVM classifier. Results The effectiveness of our methodology was evaluated using recall, logarithmic loss, accuracy (ACC), confusion matrix and area under curve (ROC) metrics. The LMGP performed best when RBF-SVM was used for the classification and gave an accuracy of 93% and 95% in the case of five-fold and ten-fold cross-validation respectively. The ten-fold cross-validation gave a precision score of 0.9410, a recall score of 0.9521, an F1 score of 0.9458, and a negative log loss of -0.1645. Conclusion LMGP combines a unique and robust approach of CT scan image feature extraction by combining local information along with gradient change in pixels of the image. The present results of the study suggest the improved performance of the LMGP method over other methods compared in this study effectively.
DOI: 10.2174/0129503752322204241127104725
Language: English
Open Access status: green
OpenAlex ID: W4408003661
FWCI:
Cites: 20
Cited by:
Related to: 10