Classification of Brain Tumor MRIs Using a Kernel Support Vector Machine

Abd-Ellah, Mahmoud Khaled; Awad, Ali Ismail; Khalaf, Ashraf A. M.; Hamed, Hesham F. A.

doi:10.1007/978-3-319-44672-1_13

Cited by 30 publications

(5 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When processing the extracted features, it becomes simpler for all the classifiers to differentiate between various classes. Extracting the most important information from raw data is known as feature extraction [31]. The extraction in our study is based on the Discrete Wavelet Transform (DWT) technique.…”

Section: Feature Extractionalgorithmmentioning

confidence: 99%

Deep Neural Network Approach Based Segmentation, Detection and Classification of Brain Tumor

Zaman

Din

et al. 2022

JERR

View full text Add to dashboard Cite

The segmentation, detection and extraction of malignant tumor regions from magnetic resonance (MR) images are challenging tasks in medical image analysis. Approaches based on machine and deep learning have been introduced, which performed better than traditional image processing methods. However, many approaches still show limited ability due to the complex dataset and image modalities. This study evaluated the deep learning approach's performance and traditional image processing algorithms for medical imaging segmentation, detection, and classification. The proposed system comprises multiple stages. The Median filters are used in the pre-processing step, and morphological operation and Otsu thresholding are used to segment MR images. Discrete Wavelet Transform (DWT) algorithm is considered in the extraction features, and their classification is executed by a convolutional neural network (CNN) and support vector machine (SVM) algorithms. The Mat lab has been used for simulation and experimental findings to evaluate the suggested method's performance on the brain's complex and highly 2D structures. The results show that the methodology is reliable and efficient, with 93.5% accuracy.

show abstract

Section: Feature Extractionalgorithmmentioning

confidence: 99%

Deep Neural Network Approach Based Segmentation, Detection and Classification of Brain Tumor

Zaman

Din

et al. 2022

JERR

View full text Add to dashboard Cite

show abstract

“…Finally achieved the best accuracy of 91.28% by using ring form partition. M. K. Abd-Ellah et al [30] detect brain tumor through MRI with machine learning model. The model used DWT and PCA for feature extraction and reduction.…”

Section: Literaturementioning

confidence: 99%

Brain Tumor Automatic Detection from MRI Images Using Transfer Learning Model with Deep Convolutional Neural Network

Bayoumi¹,

Abd-Ellah

Khalaf

et al. 2021

Journal of Advanced Engineering Trends

Self Cite

View full text Add to dashboard Cite

Brain tumor detection successfully in early-stage plays important role in improving patient treatment and survival. Evaluating magnetic resonance imaging (MRI) images manually is a very difficult task due to the numerous numbers of images produced in the clinic routinely. So, there is a need for using a computer-aided diagnosis (CAD) system for early detection and classification of brain tumors as normal and abnormal. The paper aims to design and evaluate the convolution neural network (CNN) Transfer Learning state-of-the-art performance proposed for image classification over the recent years. Five different modifications have been applied to five different famous CNN to know the most effective modification. Five-layer modifications with parameter tuning are applied for each architecture providing a new CNN architecture for brain tumor detection. Most brain tumor datasets have a small number of images to train the deep learning structure. Therefore, two datasets are used in the evaluation to ensure the effectiveness of the proposed structures. Firstly, a standard dataset from the RIDER Neuro MRI database including 349 brain MRI images with 109 normal images and 240 abnormal images. Secondly, a collection of 120 brain MRI images including 60 abnormal images and 60 normal images. The results show that the proposed CNN Transfer Learning with MRI's can learn significant biomarkers of brain tumor, however, the best accuracy, specificity, and sensitivity gained is 100% for all of them.

show abstract

“…The discovery of brain tumors has undergone substantial research and numerous discoveries have been demonstrated larger than the earlier period of two decades. In [1] the grouping of morphological segments, separate waveguides (DWT), PCA and KSVM were performed to classify MPT normally and abnormally. Further improvements were made to classify irregular images, such as cancer or cancer, by using two types of agents, Abd-Ellah et al [2].…”

Section: Related Workmentioning

confidence: 99%

Deep Weber Dominant Local Order Based Feature Generator and Improved Convolution Neural Network for Brain Tumor Segmentation in MR Images

Joseph¹,

Thomas²,

Ramya³

2020

IJEAT

View full text Add to dashboard Cite

This paper introduces a scheme for retrieving deep features to carry out the procedure of recognising brain tumors from MR image. Initially, the MR brain image is denoised through the Modified Decision Based Unsymmetric Trimmed Median Filter (MDBUTMF) after that the contrast of the image is improved through Contrast Limited Adaptive Histogram Equalization (CLAHE). Once the pre-processing task is completed, the next phase is to extract the feature. In order to acquire the features of pre-processed images, this article offers a feature extraction technique named Deep Weber Dominant Local Order Based Feature Generator (DWDLOBFG). Once the deep features are retrieved, the next stage is to separate the brain tumor. Improved Convolution Neural Network (ICNN) is used to achieve this procedure. To explore the efficiency of deep feature extraction and in-depth machine learning methods, four performance indicators were used: Sensitivity (SEN), Jaccard Index (JI), Dice Similarity Coefficient (DSC) and Positive Predictive Value (PPV). The investigational outputs illustrated that the DWDLOBFG and ICNN achieve best outputs than existing techniques.

show abstract

Classification of Brain Tumor MRIs Using a Kernel Support Vector Machine

Cited by 30 publications

References 24 publications

Deep Neural Network Approach Based Segmentation, Detection and Classification of Brain Tumor

Deep Neural Network Approach Based Segmentation, Detection and Classification of Brain Tumor

Brain Tumor Automatic Detection from MRI Images Using Transfer Learning Model with Deep Convolutional Neural Network

Deep Weber Dominant Local Order Based Feature Generator and Improved Convolution Neural Network for Brain Tumor Segmentation in MR Images

Contact Info

Product

Resources

About