A Kernel SVM Classifier for Classification of Brain Tumors in Magnetic Resonance Images

2016 ◽  
Vol 3 (3) ◽  
pp. 34
Author(s):  
RAO T. CHANDRA SEKHAR ◽  
SREENIVASULU G. ◽  
◽  
Keyword(s):  
Magnetic Resonance ◽  
Brain Tumors ◽  
Magnetic Resonance Images ◽  
Svm Classifier ◽  
Kernel Svm

scholarly journals Brain Tumor Classification of MRI Images Using Deep Convolutional Neural Network

2021 ◽  
Vol 38 (4) ◽  
pp. 1171-1179
Author(s):  
Swaraja Kuraparthi ◽  
Madhavi K. Reddy ◽  
C.N. Sujatha ◽  
Himabindu Valiveti ◽  
Chaitanya Duggineni ◽  
...  
Keyword(s):  
Neural Network ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Data Augmentation ◽  
Magnetic Resonance Images ◽  
Tumor Classification ◽  
Support Vector ◽  
Svm Classifier ◽  
Tumor Type

Manual tumor diagnosis from magnetic resonance images (MRIs) is a time-consuming procedure that may lead to human errors and may lead to false detection and classification of the tumor type. Therefore, to automatize the complex medical processes, a deep learning framework is proposed for brain tumor classification to ease the task of doctors for medical diagnosis. Publicly available datasets such as Kaggle and Brats are used for the analysis of brain images. The proposed model is implemented on three pre-trained Deep Convolution Neural Network architectures (DCNN) such as AlexNet, VGG16, and ResNet50. These architectures are the transfer learning methods used to extract the features from the pre-trained DCNN architecture, and the extracted features are classified by using the Support Vector Machine (SVM) classifier. Data augmentation methods are applied on Magnetic Resonance images (MRI) to avoid the network from overfitting. The proposed methodology achieves an overall accuracy of 98.28% and 97.87% without data augmentation and 99.0% and 98.86% with data augmentation for Kaggle and Brat's datasets, respectively. The Area Under Curve (AUC) for Receiver Operator Characteristic (ROC) is 0.9978 and 0.9850 for the same datasets. The result shows that ResNet50 performs best in the classification of brain tumors when compared with the other two networks.

scholarly journals Hybrid enhanced ICA & KSVM based brain tumor image segmentation

2018 ◽  
Vol 14 (1) ◽  
pp. 478
Author(s):  
Thrivikram Bathini ◽  
Baswaraj Gadgay
Keyword(s):  
Magnetic Resonance ◽  
Medical Image ◽  
Automated Analysis ◽  
Magnetic Resonance Images ◽  
Support Vector ◽  
Svm Classifier ◽  
Hybrid Strategy ◽  
Classifier Performance ◽  
Magnetic Resonance Imaging Mri

<span>Medical image processing is an important aspect in diagnosis and treatment strategy. The tremendous volume of medical data has accelerated the need for automated analysis of this image, more so in the case Magnetic Resonance Imaging (MRI). An improved K-means algorithm and EM algorithm have been combined in the proposed approach to produce a hybrid strategy for better clustering and segmentation using Enhanced ICA. A classifier for based on Support Vector Machine (SVM) has been formulated and employed for the classification of brain tumors in Magnetic Resonance Images (MRI). The proposed SVM classifier used a kernel in the form of Gaussian radial basis function kernel (GRB kernel) to improve the classifier performance. The performance of the classifier has been validated through expert clinical opinion and calculation of performance measures. The results amply illustrate the suitability of the proposed classifier.</span>

scholarly journals Differential Deep Convolutional Neural Network Model for Brain Tumor Classification

2021 ◽  
Vol 11 (3) ◽  
pp. 352
Author(s):  
Isselmou Abd El Kader ◽  
Guizhi Xu ◽  
Zhang Shuai ◽  
Sani Saminu ◽  
Imran Javaid ◽  
...  
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Medical Image ◽  
Resonance Imaging ◽  
Feature Maps ◽  
Deep Cnn

The classification of brain tumors is a difficult task in the field of medical image analysis. Improving algorithms and machine learning technology helps radiologists to easily diagnose the tumor without surgical intervention. In recent years, deep learning techniques have made excellent progress in the field of medical image processing and analysis. However, there are many difficulties in classifying brain tumors using magnetic resonance imaging; first, the difficulty of brain structure and the intertwining of tissues in it; and secondly, the difficulty of classifying brain tumors due to the high density nature of the brain. We propose a differential deep convolutional neural network model (differential deep-CNN) to classify different types of brain tumor, including abnormal and normal magnetic resonance (MR) images. Using differential operators in the differential deep-CNN architecture, we derived the additional differential feature maps in the original CNN feature maps. The derivation process led to an improvement in the performance of the proposed approach in accordance with the results of the evaluation parameters used. The advantage of the differential deep-CNN model is an analysis of a pixel directional pattern of images using contrast calculations and its high ability to classify a large database of images with high accuracy and without technical problems. Therefore, the proposed approach gives an excellent overall performance. To test and train the performance of this model, we used a dataset consisting of 25,000 brain magnetic resonance imaging (MRI) images, which includes abnormal and normal images. The experimental results showed that the proposed model achieved an accuracy of 99.25%. This study demonstrates that the proposed differential deep-CNN model can be used to facilitate the automatic classification of brain tumors.

scholarly journals Interobserver reliability of the classification of capitellar osteochondritis dissecans using magnetic resonance imaging

2019 ◽  
Vol 12 (4) ◽  
pp. 284-293 ◽  
Author(s):  
Rens Bexkens ◽  
F. Joseph Simeone ◽  
Denise Eygendaal ◽  
Michel PJ van den Bekerom ◽  
Luke S Oh ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Osteochondritis Dissecans ◽  
Interobserver Agreement ◽  
Interobserver Reliability ◽  
Lateral Wall ◽  
Magnetic Resonance Images ◽  
Resonance Imaging ◽  
Instability Criteria

Aim (1) To determine the interobserver reliability of magnetic resonance classifications and lesion instability criteria for capitellar osteochondritis dissecans lesions and (2) to assess differences in reliability between subgroups. Methods Magnetic resonance images of 20 patients with capitellar osteochondritis dissecans were reviewed by 33 observers, 18 orthopaedic surgeons and 15 musculoskeletal radiologists. Observers were asked to classify the osteochondritis dissecans according to classifications developed by Hepple, Dipaola/Nelson, Itsubo, as well as to apply the lesion instability criteria of DeSmet/Kijowski and Satake. Interobserver agreement was calculated using the multirater kappa (k) coefficient. Results Interobserver agreement ranged from slight to fair: Hepple (k = 0.23); Dipaola/Nelson (k = 0.19); Itsubo (k = 0.18); DeSmet/Kijowksi (k = 0.16); Satake (k = 0.12). When classifications/instability criteria were dichotomized into either a stable or unstable osteochondritis dissecans, there was more agreement for Hepple (k = 0.52; p = .002), Dipaola/Nelson (k = 0.38; p = .015), DeSmet/Kijowski (k = 0.42; p = .001) and Satake (k = 0.41; p < .001). Overall, agreement was not associated with the number of years in practice or the number of osteochondritis dissecans cases encountered per year (p > .05). Conclusion One should be cautious when assigning grades using magnetic resonance classifications for capitellar osteochondritis dissecans. When making treatment decisions, one should rather use relatively simple distinctions (e.g. stable versus unstable osteochondritis dissecans; lateral wall intact versus not intact), as these are more reliable.

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