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BRAIN TUMOUR DETECTION USING HOG BY SVM
Blekinge Institute of Technology.
Blekinge Institute of Technology.
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Detection of a brain tumour in medical images is always a challenging task. Factors like size, shape, and position of tumour vary from different patient’s brain. So, it's important to know the exact shape, size and position of a tumour in the brain making it a challenging task for detection. Some patients exhibit high glioma (HG) type tumor while others show low glioma (LG) type. So, knowing the detailed properties of a tumour to detect them in medical images is mandatory. So far many algorithms have been implemented on how to detect and extract the tumours in medical images, they used techniques such as hybrid approach with support vector machine (SVM), back propagation and dice coefficient. Among these algorithm which used back propagation as base classifier had a highest accuracy of 90%. In this work feature extraction of the medical images of patients’ tumors in database is extracted using Histogram of Oriented Gradient, later these images are classified into tumor and non tumor images using SVM. The detection of brain tumours in patient’s image is achieved by testing the performance of SVM based on Receiver Operating Characteristics (ROC). ROC include true positive rate, true negative rate, false positive rate and false negative rate. Using ROC we calculated accuracy, sensitivity and specificity values for all the medical images of the database. For image data folder of HG in vector form, SVM gave an accuracy of 97% for 95th slice of T1 modality with high true positive rate of 0.97 remaining highest among other modalities. Whereas SVM gave an accuracy of 87% for 135th slice of T1 modality with high true positive rate of 0.8 and low false positive rate of 0.06 among other image data folder of HG. For image data folder of LG, SVM gave an accuracy of 62% for the 90th slice of FLAIR modality with the high true positive rate of 0.5 and low false positive rate of 0.25 among all others. For synthetic data folder of HG, SVM gave an accuracy of 62% for a 100th slice of FLAIR modality with the high true positive rate of 0.5 and low false positive rate of 0.06 among all others. For synthetic data folder of LG, SVM gave an accuracy of 62% for a 100th slice of FLAIR modality with the high true positive rate of 0.5 and low false positive rate of 0.06 among all others.

Place, publisher, year, edition, pages
2018. , p. 48
Keyword [en]
Accuracy, Gradient, Histogram, Sensitivity, Specificity and True positive rate
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:bth-15905OAI: oai:DiVA.org:bth-15905DiVA, id: diva2:1184069
Subject / course
ET2566 Master's Thesis (120 credits) in Electrical Engineering with emphasis on Signal processing
Educational program
ETASX Master of Science Programme in Electrical Engineering with emphasis on Signal Processing
Presentation
2017-12-05, karlskrona, 13:00 (English)
Supervisors
Examiners
Available from: 2018-02-27 Created: 2018-02-20 Last updated: 2018-02-27Bibliographically approved

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Blekinge Institute of Technology
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CiteExportLink to record
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Citation style
  • apa
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Output format
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