Detection of normal and abnormal brain tumor MRI images using machine learning approaches
Authors: Srikanth Royyuru , Kanya N. , Kumar P. S. Raja 
Published: February 2024

<p>Without image processing, the modern technological world we live in today would be completely different. Different applications can be found in many different areas, including as medicine, remote sensing, computer vision, and more. Brain tumours occur because of the abnormal growth of brain tissue. Therefore, it is crucial to surgically remove the tumor’s component that has spread throughout the brain. The creation of images of the human body’s internal organs and structures depends on numerous factors, one of which is the use of Magnetic Resonance Imaging (MRI), one of the many medical imaging techniques. Segmenting images is a challenging task in the modern medical field. The tumor’s location and sizes can be detected with more accuracy with the help of a segmentation performed on MRI slices. In this paper, we propose the Adaptive Convex Region Contour (ACRC) algorithm, a novel method for achieving such requirements. Here, we use the Support Vector Machine, often known as SVM, to identify slices and determine whether they are normal or abnormal. Once the SVM results are in, the off-kilter slices will be included into the analysis. Since the human body already has a complex anatomy in 3D. It’s disappointing because MRI slices can only provide images in a plain, 2D plane. Because of its 3D structure, the tumor’s actual shape cannot be detected from a surface image. This means that the transformation from 2D to 3D is important, as it provides necessary data to the surgeons doing the operation. The Rapid Mode Image Matching (RMIM) algorithm should be used when constructing a 3D reconstruction model. Immediately after the 3D model is completed being created, a rough estimate of the tumor’s original volume is generated. The methodological consideration was conducted in the simulated MATLAB software. It was determined from the data that the proposed method is better to the current establishment methodologies in terms of accuracy. <strong>Objective:</strong> This paper aims to address the need for accurate segmentation of brain tumors in Magnetic Resonance Imaging (MRI) slices. The primary research question revolves around developing a novel segmentation method, the Adaptive Convex Region Contour (ACRC) algorithm, to improve the precision of tumor detection by incorporating a Support Vector Machine (SVM) for identifying abnormal slices.<strong> </strong><strong>Methods: </strong>The proposed approach involves utilizing the SVM to classify MRI slices as either normal or abnormal. The abnormal slices, detected by SVM, are subjected to further analysis. To overcome the limitation of MRI producing 2D images for the inherently 3D human anatomy, a transformation from 2D to 3D is crucial. The Rapid Mode Image Matching (RMIM) algorithm is adopted to construct a 3D reconstruction model of the brain and its tumor. This model allows surgeons to obtain a more accurate understanding of the tumor’s actual 3D shape. <strong>Results: </strong>The methodology was implemented and evaluated using simulated MATLAB software. The results demonstrate that the proposed ACRC algorithm, combined with SVM-based abnormal slice identification and subsequent 3D reconstruction using RMIM, outperforms existing methodologies in terms of accuracy. The 3D reconstruction provides valuable insights into the tumor’s shape, aiding surgical planning. <strong>Conclusions: </strong>In conclusion, the research contributes to the field of medical image processing by presenting a comprehensive approach for brain tumor segmentation and 3D reconstruction. The ACRC algorithm, in conjunction with SVM and RMIM, enhances the accuracy of tumor detection and provides critical 3D information for surgical procedures. This advancement holds the potential to improve the efficacy of brain tumor surgeries, underscoring the significance of innovative image processing techniques in modern medical applications.</p>