Automated Detection and Classification of Brain Tumor From MRI Images using Machine Learning Methods

Abstract

The focus of this thesis is to report an automated, efficient, and robust method of brain tumor detection and classification from Magnetic Resonance Images (MRI) images. Clinically, it is a challenging issue faced by the researchers working in this domain. In routine health care units, Magnetic Resonance (MR) scanners are being used to generate a massive number of brain slices, underlying the anatomical details. Pathological assessment from this medical data is being carried out manually by the radiologists or neurooncologists. Due to huge volume of brain anatomical data produced by MRI scanners, it is almost impossible to manually analyze every slice. Conclusively, if automated protocols are executed for auto-interpretation; not only the radiologist will be assisted but also a better pathological assessment process would be expected. Several methods have been suggested to address this problem, but still, accuracy, robustness and optimization is still an open issue to address. The development of such automated procedures is difficult due to complex organization of brain cells, several types of tumor, difference in medical traits of a specific ethnicity and many more factors. To achieve the target, research has been started from reviewing the most popular and prominent state-of-the-art methods. Based upon the reviewed literature, automated brain tumor detection and classification techniques have been reported with high computational cost, low classification rates, detection and classification of only one or a few of brain tumor types, lack of robustness, etc. Therefore, step wise research and experiments based upon empirical scientific methodology have been performed in order to achieve the objectives of brain tumor classification. In the first step, a research activity has been performed to report a colorization method with the aims to enhance the visualization, cell characterization and interpretation of brain cells. The high dimensional brain data scanned through MRI embodied in gray scale, if converted, represented, mapped and/or visualized in colored versions, irrefutably, more definitive and more accurate the pathological assessment process will be. Several methods have been reported to represent brain MRI data in color with high computational complexity. In this research activity, an efficient method of colorization using frequencies from visible range of color spectrum, has been proposed to embody the variations and sensitivity of the brain MRI images. The experiments have been performed on a locally developed dataset. Side by side visual comparison based on multiple MRI sequences of identical subjects by domain experts have proved the adequate success and fruitfulness of the story. The reported method of colorization as a protocol has also been deployed in Department of Radiology and Diagnostic Images, Bahawal Victoria, Hospital, Bahawalpur (BVHB), Pakistan. Radiologists are using this tool for visual interpretation and monitoring of the patients for their assessment and clinical decision making. In second step, an automated approach using Gabor filter and Support Vector Machines (SVMs), for the classification of brain MRI slices as normal or abnormal has been reported. Accuracy, sensitivity, specificity and AUC-value have been used as standard quantitative measures to evaluate the proposed algorithm. To the best of our knowledge, this is the first study in which experiments have been performed on The Whole Brain Atlas - Harvard Medical School (HMS) dataset, achieving an accuracy of 97.5%, sensitivity of 99%, specificity of 92% and AUC-value as 0.99. To test the robustness against medical traits based on ethnicity and to achieve optimization, a locally developed dataset has also been used for experiments and remarkable results with accuracy (96.5%), sensitivity (98%), specificity (92%) and AUC-value (0.97) were achieved. Comparison with state-of-the art methods proved the overall efficacy of the proposed method. In third step of the thesis, a method has been proposed to classify brain MRI image into brain related disease groups and further tumor types. The proposed method employed Gabor texture followed by a set of more distinguished statistical features. These features are then used by SVM to classify the brain disorder. K-fold strategy has been adapted for cross validation of the results to enhance generalization of SVM. Experiments have been performed to classify brain MRI images as normal or belonging to either of the common diseases, such as cerebrovascular, degenerative, inflammatory, and neoplastic. Neoplastic disease is further classified into glioma, meningioma, metastatic adenocarcinoma, metastatic bronchogenic carcinoma, or sarcoma. Standard quantitative evaluation measures, i.e., accuracy, specificity, sensitivity, and AUC-value have been used to test performance of the developed system. The proposed system has been trained on complete dataset of HMS, so the trained model has the ability to deal with a wide range of brain abnormalities. Further, to achieve robustness, a locally developed dataset has also been used for experiments. Remarkable results on different orientations, sequences of both of these datasets as per accuracy (up-to 99.6%), sensitivity (up-to 100%), specificity (up-to 100%), precision (up-to 100%) and AUC-value (up-to 1.0) have been achieved. The proposed method classifies the brain MRI slices into defined abnormality groups. It can also classify the abnormal slices into tumorous or non-tumorous one. The major achievement of the developed system is its auto classification of tumorous slices into the slices having primary tumor or secondary tumor and their further types, which possibly could not be determined without biopsy. In fourth step of the thesis, results achieved through the proposed method of brain tumor classification have been validated on cross data set. The drive of this research activity is to verify the robustness of the reported approach. For this, the model has been trained completely on one data set, while tested completely on another one. A benchmarked dataset HMS and a locally developed dataset BVHB dataset has been used for this purpose. To ensure its robustness, complete HMS dataset was used to train the model and BVHB was used to test the trained model and vice versa. Standard evaluation measures, i.e., accuracy, specificity, sensitivity, precision and AUC-value have been used to evaluate the system. It has been established that the proposed method deals with multiformity and variability of brain MRI data. Overall, suppositions regarding robustness of the proposed method were attained with maximum measures as per accuracy as 92%, specificity as 92%, sensitivity as 93%, precision as 92%, and AUC-value as 0.93. The overall results achieved through the proposed method, manifests that it is robust, efficient and reliable. It has been trained on a large volume of multi-orientations, multi-sequences belonging to multi-datasets to deal with multiformity and to face variability.