MRI Imaging for the Detection and Diagnosis of Brain Tumors

As soon as abnormal brain cells grow and have the potential to develop into cancerous cells, this condition is referred to as a brain tumor. Brain lesions can either be primary or secondary. Primary tumors start in the brain, whereas secondary (metastatic) tumors move from other organs like the lung, breast, or kidney to the brain. Among people's most common brain tumors are meningiomas, gliomas, pituitary adenomas, oligodendrogliomas, and vestibular schwannoma. This research proposes a novel method for MRI image-based brain tumor detection. The SIFT method and the SVM algorithm are used in the suggested method to extract features. The SIFT technique is used to extract details from the MRI images that can differentiate between tumor and normal brain tissue. An SVM classifier is then trained to differentiate between normal and tumor MRI images using the extracted characteristics. The experimental findings show that the suggested strategy achieves high accuracy in tumor detection. The proposed method is tested on a dataset of MRI images. The findings suggest that a potential method for the early diagnosis and detection of brain tumors using MRI images is the combination of the SVM algorithm and the SIFT feature extraction methodology. This research makes a contribution to the field of medical image analysis and offers clinicians a useful tool to increase the precision and effectiveness of brain tumor detection.