Brain cancer is considered one of the most dangerous and most common types of cancer. Hence, research focused on improving the quality of brain images in a non-intrusive manner, which is magnetic resonance imaging. MRI depends on stimulating water molecules’ protons in the human body and hitting them with radio waves, where the protons respond to this stimulation by generating radio energy [1].
Along with the significant development in the medical image processing field, image processing algorithms have become an essential part of the MRI device software, from simple operations such as contrast control, edge detection, and gray-level transformations, to image segmentation, classification, and brain image diagnosis [2].
In the past couple years, several critical studies have been conducted in this field. In 2019, a study entitled “Brain tumor classification using MRI images with K-nearest neighbor method” was launched [3], this study detected brain tumors and classified the tumor into three types using watershed segmentation and K-nearest neighbor (KNN) classification algorithm, but the accuracy reached was 89%, which is not sufficient.
This was followed by a study in 2020 entitled “Detection and classification of brain tumor using support vector machine-based GUI” [4], this study relied on wavelet transformation to extract features and used principal component analysis (PCA) technology to reduce their number. A graphical user interface (GUI) was also designed to display the processing results. One of the disadvantages of this study is that the accuracy was not calculated to determine its success, in addition to that, the designed GUI displays the values of the extracted features which do not mean much to the user.
The same year, a study entitled “Semantic segmentation of brain tumor MRI images and SVM classification using GLCM features” was published [5]. This study also used the watershed segmentation technique, extracted gray level co-occurrence matrix (GLCM) features, and then compared the results of classification using six support vector machine (SVM) classifiers, the highest classification accuracy was for both the linear SVM and the quadratic SVM, with 93%. One of the disadvantages of this study is that it used only 36 images for training, which is not an adequate number, where employing additional features such as shape features could have raised the accuracy further.
The above research papers represent the gold standard we have attempted to outperform by applying a method based on several algorithms that achieved higher accuracy than all previous similar studies.
The primary motive of the research is the extremely large number of medical images that consumes a lot of time and effort to diagnose, and the inability of the clinician sometimes to determine all suspicious areas in the image, in addition to the lack of previous studies that reached a satisfactory result, so we designed an innovative hybrid algorithm, in which we relied on a database of 150 cross-sectional MRI images of the brain.
We followed a methodology that consists of two main stages:
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1.
Classifying magnetic resonance images of the brain into images with or without a tumor and displaying the tumor if it exists.
This stage consists of several steps: preprocessing and enhancement, segmentation, feature extracting, and classification in a hybrid manner based on the results of three classifiers combined. This method reached an accuracy of 96.6%.
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2.
Designing and programming a graphical user interface and a standalone application using MATLAB 2018a.
The importance of this research lies in the ability to diagnose a large number of images in a short period of time, thus reducing the burden on the doctor, it also gives more accurate diagnoses because of its ability to distinguish areas that may not be visible to the naked eye, in addition to the possibility of using these programs in training medical students to diagnose images and identify suspicious areas.