Abstract: A brain tumor is a kind of abnormal development caused by unregularized cell reproduction and it is increasing day-by-day. The Magnetic Resonance Imaging (MRI) tools are the most often used diagnostic tool for brain tumor detection. However, ample amount of information contained in MRI makes the detection and analysis process tedious and time consuming. The ability to accurately identify the exact size and proper location of a brain tumor is a tough task for radiologists. Medical image processing is an interdisciplinary discipline in which image processing is a tough research. Image segmentation is the prime requirement in image processing as it separates dubious regions from biomedical images thereby enhancing the treatment reliability. In this regard, our article reviews eight existing binary classifiers to compare their results for designing an automated Computer Aided Diagnosis (CAD) system. The proposed classification models can analyze T1-weighted brain MRI images to reach at a conclusion. The classification accuracy advocates the quality of our work.

Mitrabinda Khuntia, Prabhat Kumar Sahu and Swagatika Devi, “Prediction of Presence of Brain Tumor Utilizing Some State-of-the-Art Machine Learning Approaches” International Journal of Advanced Computer Science and Applications(IJACSA), 13(5), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130595

@article{Khuntia2022,
title = {Prediction of Presence of Brain Tumor Utilizing Some State-of-the-Art Machine Learning Approaches},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130595},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130595},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {5},
author = {Mitrabinda Khuntia and Prabhat Kumar Sahu and Swagatika Devi}
}