Brain image fusion-based tumour detection using grey level co-occurrence matrix Tamura feature extraction with backpropagation network classification.

Most challenging task in medical image analysis is the detection of brain tumours, which can be accomplished by methodologies such as MRI, CT and PET. MRI and CT images are chosen and fused after preprocessing and SWT-based decomposition stage to increase efficiency. The fused image is obtained through ISWT. Further, its features are extracted through the GLCM-Tamura method and fed to the BPN classifier. Will employ supervised learning with a non-knowledge-based classifier for picture classification. The classifier utilized Trained databases of the tumour as benign or malignant from which the tumour region is segmented via k-means clustering. After the software needs to be implemented, the health status of the patients is notified through GSM. Our method integrates image fusion, feature extraction, and classification to distinguish and further segment the tumour-affected area and to acknowledge the affected person. The experimental analysis has been carried out regarding accuracy, precision, recall, F-1 score, RMSE and MAP.