Automated Detection of Brain Tumor through Magnetic Resonance Images Using Convolutional Neural Network

Gull, Sahar; Akbar, Shahzad; Khan, Habib Ullah

doi:10.1155/2021/3365043

Cited by 52 publications

(17 citation statements)

References 56 publications

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“…They attained 94% recognition rate against the MRI dataset. Furthermore, a novel framework was designed by [ 15 ] for the brain tumor detection against various MRI datasets. This framework was based on transfer learning methods and fully convolutional neural network model, which has five steps such as pre- and post-processing, skull denudation, segmentation, and classification.…”

Section: Introductionmentioning

confidence: 99%

A Precise Medical Imaging Approach for Brain MRI Image Classification

Ṣiddiqi

Alsayat

Alhwaiti

et al. 2022

Computational Intelligence and Neuroscience

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Magnetic resonance imaging (MRI) is an accurate and noninvasive method employed for the diagnosis of various kinds of diseases in medical imaging. Most of the existing systems showed significant performances on small MRI datasets, while their performances decrease against large MRI datasets. Hence, the goal was to design an efficient and robust classification system that sustains a high recognition rate against large MRI dataset. Accordingly, in this study, we have proposed the usage of a novel feature extraction technique that has the ability to extract and select the prominent feature from MRI image. The proposed algorithm selects the best features from the MRI images of various diseases. Further, this approach discriminates various classes based on recursive values such as partial Z-value. The proposed approach only extracts a minor feature set through, respectively, forward and backward recursion models. The most interrelated features are nominated in the forward regression model that depends on the values of partial Z-test, while the minimum interrelated features are diminished from the corresponding feature space under the presence of the backward model. In both cases, the values of Z-test are estimated through the defined labels of the diseases. The proposed model is efficiently looking the localized features, which is one of the benefits of this method. After extracting and selecting the best features, the model is trained by utilizing support vector machine (SVM) to provide the predicted labels to the corresponding MRI images. To show the significance of the proposed model, we utilized a publicly available standard dataset such as Harvard Medical School and Open Access Series of Imaging Studies (OASIS), which contains 24 various brain diseases including normal. The proposed approach achieved the best classification accuracy against existing state-of-the-art systems.

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Section: Introductionmentioning

confidence: 99%

A Precise Medical Imaging Approach for Brain MRI Image Classification

Ṣiddiqi

Alsayat

Alhwaiti

et al. 2022

Computational Intelligence and Neuroscience

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“…The interpretability and generalizability of automated models require improvements in study quality and design, including external validation. Sahar Gull et al [ 25 ] stated that for automated segmentation and classification of brain tumors, the DL-based model is suggested. The proposed framework effectively and precisely identifies brain tumors from MR images.…”

Section: Related Workmentioning

confidence: 99%

Grade Classification of Tumors from Brain Magnetic Resonance Images Using a Deep Learning Technique

et al. 2023

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To improve the accuracy of tumor identification, it is necessary to develop a reliable automated diagnostic method. In order to precisely categorize brain tumors, researchers developed a variety of segmentation algorithms. Segmentation of brain images is generally recognized as one of the most challenging tasks in medical image processing. In this article, a novel automated detection and classification method was proposed. The proposed approach consisted of many phases, including pre-processing MRI images, segmenting images, extracting features, and classifying images. During the pre-processing portion of an MRI scan, an adaptive filter was utilized to eliminate background noise. For feature extraction, the local-binary grey level co-occurrence matrix (LBGLCM) was used, and for image segmentation, enhanced fuzzy c-means clustering (EFCMC) was used. After extracting the scan features, we used a deep learning model to classify MRI images into two groups: glioma and normal. The classifications were created using a convolutional recurrent neural network (CRNN). The proposed technique improved brain image classification from a defined input dataset. MRI scans from the REMBRANDT dataset, which consisted of 620 testing and 2480 training sets, were used for the research. The data demonstrate that the newly proposed method outperformed its predecessors. The proposed CRNN strategy was compared against BP, U-Net, and ResNet, which are three of the most prevalent classification approaches currently being used. For brain tumor classification, the proposed system outcomes were 98.17% accuracy, 91.34% specificity, and 98.79% sensitivity.

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“…On the other hand, tumors that first develop before spreading to the brain are referred to as secondary tumors [11]. Brain tumor detection and classification can be done using a variety of imaging techniques, but MRI imaging technique is the popular method for brain tumor detection [5]. The frequent kind of brain tumor with glial cell origins is gliomas [25].…”

Section: Introductionmentioning

confidence: 99%

Multi-class Brain Tumor Classification and Segmentation using Hybrid Deep Learning Network Model

Kumar

Bonthu

Meghana

et al. 2023

SCPE

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Brain tumor classification is a significant task for evaluating tumors and selecting the type of treatment as per their classes. Brain tumors are diagnosed using multiple imaging techniques. However, MRI is frequently utilized since it provides greater image quality and uses non-ionizing radiation. Deep learning (DL) is a subfield of machine learning and recently displayed impressive performance, particularly in segmentation and classifying problems. Based on convolutional neural network (CNN), a Hybrid Deep Learning Network (HDLN) model is proposed in this research for classifying multiple types of brain tumors including glioma, meningioma, and pituitary tumors. The Mask RCNN is used for brain tumor classification. We used a squeeze-and-excitation residual network (SE-ResNet) for brain tumor segmentation, which is a residual network (ResNet) with a squeeze-and-excitation block. A publicly available research dataset is used for testing the proposed model for experiment analysis and it obtained an overall accuracy of 98.53%, 98.64% sensitivity and 98.91% specificity. In comparison to the most advanced classification models, the proposed model obtained the best accuracy. For multi-class brain tumor diseases, the proposed HDLN model demonstrated its superiority to the existing approaches.

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Automated Detection of Brain Tumor through Magnetic Resonance Images Using Convolutional Neural Network

Cited by 52 publications

References 56 publications

A Precise Medical Imaging Approach for Brain MRI Image Classification

A Precise Medical Imaging Approach for Brain MRI Image Classification

Grade Classification of Tumors from Brain Magnetic Resonance Images Using a Deep Learning Technique

Multi-class Brain Tumor Classification and Segmentation using Hybrid Deep Learning Network Model

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