Brain Tumor Classification Using Image Fusion and EFPA-SVM Classifier

An accurate and early diagnosis of brain tumors based on medical imaging modalities is great interest because are a harmful threat to person’s health worldwide. Several techniques have been used analyze tumors, including computed tomography (CT) magnetic resonance (MRI). CT provides information about dense tissues, whereas MRI gives soft tissues. However, the fusion images has little effect enhancing accuracy tumors. Therefore, machine learning methods adopted diagnose in recent years. This paper intends develop novel scheme detect classify fused images. The proposed approach starts with preprocessing reduce noise. Then, rules applied get image, segmentation algorithm employed isolate tumor region from background region. Finally, classifier classified into benign malignant Computing statistical measures evaluate classification potential scheme. Experimental outcomes provided, Enhanced Flower Pollination Algorithm (EFPA) system shows that it outperforms other considered for comparison.