Search or add a thesis

Advanced Search (Beta)
Home > Automated Detection and Classification of Brain Tumor from Mri Images Using Machine Learning Methods

Automated Detection and Classification of Brain Tumor from Mri Images Using Machine Learning Methods

Thesis Info

Access Option

External Link

Author

Gilanie, Ghulam

Program

PhD

Institute

COMSATS University Islamabad

City

Islamabad

Province

Islamabad.

Country

Pakistan

Thesis Completing Year

2019

Thesis Completion Status

Completed

Subject

Computer Science

Language

English

Link

http://prr.hec.gov.pk/jspui/bitstream/123456789/11158/1/PhD%20Thesis%20Ghulam%20Gilanie.pdf

Added

2021-02-17 19:49:13

Modified

2024-03-24 20:25:49

ARI ID

1676727707584

Similar


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.
Loading...
Loading...

Similar Books

Loading...

Similar Chapters

Loading...

Similar News

Loading...

Similar Articles

Loading...

Similar Article Headings

Loading...

لگدا اے سکھ پریڑے ہو گئے

لگدا اے سکھ پریڑے ہو گئے
دکھ ہن چار چوفیرے ہو گئے
نہیں پرندے لہندے گھر وچ
اُچے ڈھیر بنیرے ہو گئے
بُھج کلیجہ زخمی ہویا
سیکھاں اتے بیرے ہو گئے
وچ درگاہ دے رتبے پاندے
عاجز ڈھیر نویرے ہو گئے
جتھے حسن بہاراں آئیاں
اوتھے عشق دے ڈیرے ہو گئے
جنھاں وفا نہ میں نال کیتی
سکے کیویں اوہ تیرے ہو گئے

غم دی رات ہجر دی لمی
اتوں گھپ ہنیرے ہو گئے
کیویں ختم غلامی منّاں
جدوں غلام پتھیرے ہو گئے
رُکھ تاں سارے کٹ دتے نیں
پکھواں کتھ بسیرے ہو گئے
سجناں رات غماں دی گھلی
قسمت نال سویرے ہو گئے
میں باغی ہاں اس مسکن دا
منصف جتھ وڈیرے ہو گئے
واہی ہجر دی برہوں فصلاں
اتوں غم دے کیرے ہو گئے
دکھاں دی پنڈ چاون والے
ساتھی کئی ہن میرے ہو گئے

PENDIDIKAN MORAL PESERTA DIDIK MELALUI IMPLEMENTASI TATA TERTIB SEKOLAH DASAR NEGERI INPRES BUMI BAHARI KECAMATAN PALU BARAT

An Efforts to improve the morality of learners are always conducted in education. Educational institutions are morally obligated to increase personality development of their students. SDN Inpres Bumi Bahari Kec. Palu Barat through the implementation of school rules, intends to form the character of learners. This circumstance is the basis of this research. This research based on descriptive qualitative methods. The Source of data obtained from principals, teachers, and Learners. This research used varieties methods such as observation, interviews, and documentations in data collecting process. Furthermore, data that has been netted, analyzed using data reduction techniques, data display, and data verification. The results of the study concluded that the application of school rules can shape the behavior of learners to be a good personality. The behavior of learners is directed towards moral learner. The process of character building of learners finds some barriers because learners have a different understanding of school rules order. In addition, many students have a strong influence of the habits they bring from their homes, so they are not familiar with the rules set by their school.

Physiological Characteristics of Testicular Venous Blood Flow and Associated Structural Changes in Individuals With Varicocele

Varicocele is known to be associated with infertility and sperm disorders. The exact mechanism behind these mal effects is yet not clear, especially the relationship of testicular blood flow and semen quality parameters. Objective of the study was to evaluate testicular blood flow pattern and spermatic cord microanatomy of infertile individuals with varicocele and to find their correlation/association with semen quality. Sixty consecutive patients between 20–45 years age, diagnosed with infertility and varicocele, undergoing microsurgical varicocelectomy at Fatima Memorial Hospital Lahore, were included in the study. Semen quality parameters and scrotal color Doppler ultrasonography (CDUS) were recorded preoperatively. During surgery, microanatomy of the spermatic cord was observed. The outcomes of semen analysis were sperm count, motility and morphology. The testicular blood flow was determined as peak systolic velocity (PSV) and resistive index (RI) of sub capsular artery and intra parenchymal artery of the testes by scrotal CDUS. During microsurgery, anatomy of varicocele veins, testicular artery, arterio-venous micro communications, lymphatics and their arrangement was recorded. Result revealed that of 60 patients, mean sperm count, progressive motility and morphology were 29.6±24.26 million/mL, 16.47±14.53% and 5.18±5.47% respectively. The mean varicocele diameters were 3.26±0.85 and 3.72 ± 1.10 mm at lying position and at standing posture with valsalva maneuver respectively. No significant correlation was found between semen quality parameters and varicocele vein diameter. Similarly there was no correlation between the sperm count and testicular blood flow parameters. A negative correlation was found between progressive