Classification of normal and abnormal brain MRI slices using Gabor texture and support vector machines

Gilanie, Ghulam; Bajwa, Usama Ijaz; Waraich, Mustansar Mahmood; Habib, Zulfiqar; Ullah, Hafeez; Nasir, Muhammad

doi:10.1007/s11760-017-1182-8

Cited by 30 publications

(21 citation statements)

References 22 publications

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“…However, in this study like rest of the studies, we have used the same dataset and implemented image-level classification. It can be noted from Table 4 that, DWT (Chaplot et al, 2006;El-Dahshan et al, 2010;Nayak et al, 2016) , Gabor texture (Gilanie et al, 2018), local binary pattern (LBP) (Hooda and Verma, 2019), stationary WT (SWT) (El-Dahshan and Bassiouni, 2018), kernel linear discriminator analysis KLDA) (El-Dahshan and Bassiouni, 2018), shearlet transform (Gudigar et al, 2019-a) methods and variational mode decomposition (VMD) (Gudigar et al, 2019-b) were used for feature extraction from images. The PCA (El-Dahshan et al, 2010;El-Dahshan and Bassiouni, 2018;Nayak et al, 2016;Zhang et al, 2011) method was applied for the reduction of features dimensions.…”

Section: Discussionmentioning

confidence: 99%

Convolutional neural networks for multi-class brain disease detection using MRI images

Talo

Yıldırım

Baloğlu

et al. 2019

Computerized Medical Imaging and Graphics

231

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The brain disorders may cause loss of some critical functions such as thinking, speech and movement. So, the early detection of brain diseases may help to get the timely best treatment. One of the common methods used to diagnose these disorders is the magnetic resonance imaging (MRI) technique. Manual diagnosis of brain abnormalities is time-consuming and difficult to perceive the minute changes in the MRI images especially in the early stages of abnormalities. Proper selection of the features and classifiers to obtain the highest performance is a challenging task. Hence, deep learning models have been widely used for medical image analysis over the past few years. In this study, we have employed the AlexNet, Vgg-16, ResNet-18, ResNet-34 and ResNet-50 pre-trained models to automatically classify MR images in to normal, cerebrovascular, neoplastic, degenerative, and inflammatory diseases classes. We have also compared their classification performance with pre-trained models which are the state-of-art architectures. We have obtained the best classification accuracy of 95.23% ± 0.6 with ResNet-50 model among the five pre-trained models. Our model is ready to be tested with huge MRI images of brain abnormalities. The outcome of model will also help the clinicians to validate their finding after manual reading of the MRI images.

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Section: Discussionmentioning

confidence: 99%

Convolutional neural networks for multi-class brain disease detection using MRI images

Talo

Yıldırım

Baloğlu

et al. 2019

Computerized Medical Imaging and Graphics

231

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“…Classification rates as per accuracy remained 99%, which was appreciative but did not further classify the neoplastic types. Our proposed study has been conducted in continuation to the earlier work 13 in which, an automated approach for brain MRI slices classification using Gabor Texture and SVMs was reported. The results were appreciative; however, the reported method was limited to classify the brain MRI slices as normal or abnormal.…”

Section: Introductionmentioning

confidence: 91%

Computer aided diagnosis of brain abnormalities using texture analysis of MRI images

Gilanie

Bajwa

Waraich

et al. 2019

Int J Imaging Syst Tech

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The drive of this study is to develop a robust system. A method to classify brain magnetic resonance imaging (MRI) image into brain‐related disease groups and tumor types has been proposed. The proposed method employed Gabor texture, statistical features, and support vector machine. Brain MRI images have been classified into normal, cerebrovascular, degenerative, inflammatory, and neoplastic. The proposed system has been trained on a complete dataset of Brain Atlas‐Harvard Medical School. Further, to achieve robustness, a dataset developed locally has been used. Extraordinary 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 tumorous slices are further classified into primary or secondary tumor as well as their further types as glioma, sarcoma, meningioma, bronchogenic carcinoma, and adenocarcinoma, which could not be possible to determine without biopsy, otherwise.

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“…Consequently, a supplementary investigation is compulsory to inspect suitable means of classification, which could deal with variability and multiformity present in MRI data. This research activity has been conducted in continuation to our previous work, 11 in which, an optimized method to classify brain MRI slices as normal or abnormal is presented. The reported approach exploited the texture of brain MRI images using Gabor filter and then support vector machines have been used to train a model in supervised fashion.…”

Section: Introductionmentioning

confidence: 98%

Automated and reliable brain radiology with texture analysis of magnetic resonance imaging and cross datasets validation

Gilanie

Bajwa

Waraich

et al. 2019

Int J Imaging Syst Tech

Self Cite

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Reliable brain tumor radiology is one of the serious mortality issues of medical hospitals and on priority of healthcare departments. In this research, the presence of brain tumor and its type (if exists) is automatically diagnosed from magnetic resonance imaging (MRI). The first step is most important where suitable parameters from Gabor texture analysis are extracted and then classified with a support vector machine. The drive of this research activity is to verify robustness of the proposed model on cross datasets, so that it could deal with variability and multiformity present in MRI data. Further to this, the developed approach is able to deploy as a real application in the local environment. Therefore, once a model has been trained and tested on an openly available benchmarked dataset, it is retested on a different dataset acquired from a local source. Standard evaluation measures, that is, accuracy, specificity, sensitivity, precision, and AUC‐values have been used to evaluate the robustness of the proposed method. It has been established that the proposed method has the ability to deal with multiformity, variability, and local medical traits present in brain MRI data.

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Classification of normal and abnormal brain MRI slices using Gabor texture and support vector machines

Cited by 30 publications

References 22 publications

Convolutional neural networks for multi-class brain disease detection using MRI images

Convolutional neural networks for multi-class brain disease detection using MRI images

Computer aided diagnosis of brain abnormalities using texture analysis of MRI images

Automated and reliable brain radiology with texture analysis of magnetic resonance imaging and cross datasets validation

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