A Novel MRI Diagnosis Method for Brain Tumor Classification Based on CNN and Bayesian Optimization

Amou, Mohamed Ait; Xia, Kewen; Kamhi, Souha; Mouhafid, Mohamed

doi:10.3390/healthcare10030494

Cited by 51 publications

(27 citation statements)

References 39 publications

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“…Non-invasive brain magnetic resonance imaging (MRI) is the most often used tool for detecting and tracking the progression of brain tumors. Compared to computed tomography, an MRI image provides precise information on brain anatomy [ 7 ]. While examining the numerous MRI slices for patients with brain tumors, radiologists find brain malignancies.…”

Section: Introductionmentioning

confidence: 99%

Isolated Convolutional-Neural-Network-Based Deep-Feature Extraction for Brain Tumor Classification Using Shallow Classifier

et al. 2022

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In today’s world, a brain tumor is one of the most serious diseases. If it is detected at an advanced stage, it might lead to a very limited survival rate. Therefore, brain tumor classification is crucial for appropriate therapeutic planning to improve patient life quality. This research investigates a deep-feature-trained brain tumor detection and differentiation model using classical/linear machine learning classifiers (MLCs). In this study, transfer learning is used to obtain deep brain magnetic resonance imaging (MRI) scan features from a constructed convolutional neural network (CNN). First, multiple layers (19, 22, and 25) of isolated CNNs are constructed and trained to evaluate the performance. The developed CNN models are then utilized for training the multiple MLCs by extracting deep features via transfer learning. The available brain MRI datasets are employed to validate the proposed approach. The deep features of pre-trained models are also extracted to evaluate and compare their performance with the proposed approach. The proposed CNN deep-feature-trained support vector machine model yielded higher accuracy than other commonly used pre-trained deep-feature MLC training models. The presented approach detects and distinguishes brain tumors with 98% accuracy. It also has a good classification rate (97.2%) for an unknown dataset not used to train the model. Following extensive testing and analysis, the suggested technique might be helpful in assisting doctors in diagnosing brain tumors.

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Section: Introductionmentioning

confidence: 99%

Isolated Convolutional-Neural-Network-Based Deep-Feature Extraction for Brain Tumor Classification Using Shallow Classifier

et al. 2022

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“…The outcomes using RCNN as per average confidence score is 98.83%. This study [23] offers an effective Bayesian Optimization-based technique for CNN hyperparameter optimization. Figshare brain tumor dataset with 3064 T1C MRI images of brain tumors, including Glioma, Meningioma, and Pituitary has been used for research and experiments.…”

Section: Literature Reviewmentioning

confidence: 99%

“…these two studies are unable to determine the grades of brain tumors. In this study [23], Bayesian Optimization-based technique is used for CNN hyperparameter optimization using the Figshare brain tumor dataset with an accuracy of 98.70%. In this study [24], a VGG19 model is used for brain tumor detection using Figshare dataset with an accuracy of 99.83%.…”

Section: As Represented Inmentioning

confidence: 99%

A CNN-Model to Classify Low-Grade and High-Grade Glioma From MRI Images

et al. 2023

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Glioma is the most occurring brain tumor in the world. Its grade (level of severity) identification, crucial in its treatment planning, is most demanding in a clinical environment. Computeraided methods have been experimented with to identify the grade of glioma, out of which deep learning-based methods, due to their auto features engineering, have a good impact in terms of their achieved outcomes. In this study, convolutional neural networks (CNNs) have been explored and utilized for the classification of glioma grading, for example, low grade (grade I-II) and high grade (grade III-IV). A CNN-based model, which is light-weighted in terms of layers, size, and learnable parameters, has been proposed. Experimental tests were carried out on benchmarked publicly available datasets, for example, Brats-2017, Brats-2018, & Brats-2019. A locally developed dataset from Bahawal Victoria Hospital, Bahawalpur, Pakistan, has also been employed for experimentation and research to cross-validate the outcomes. Additionally, experiments have been carried out to compare the effectiveness of the proposed model, and results have been compared with the results of state-of-the-art pertained CNN models, i.e., resnet18, squeezenet, and alexnet. The proposed model achieved maximum standard evaluation measures on the benchmarked dataset, i.e., accuracy, specificity, and sensitivity at 97.85%, 98.88%, and 99.88%, respectively. Similarly, these measures were 98.89%, 99.28%, and 99.77% on a locally developed dataset, which is the best compared to the recent stateof-the-art related studies.INDEX TERMS Low and high-grade glioma grading, convolutional neural networks, MRI images.

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“…They suggested an effective hyperparameter optimization method for CNN based on Bayesian optimization in [107]. By categorizing 3064 T1 images into three different types of brain cancers (glioma, pituitary, and meningioma), this method was assessed.…”

Section: Mri Brain Tumor Classification Using DLmentioning

confidence: 99%

A Review of Brain Tumor Diagnosis and Segmentation

Kaifi¹

2023

Preprint

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Uncontrolled and fast cell proliferation is the cause of brain tumors. Early cancer detection is vitally important in order to save many lives. Brain tumors can be divided into several categories depending on the kind, place of origin, pace of development, and stage of progression; as a result, tumor classification is crucial for targeted therapy. The aim of brain tumor segmentation is to accurately delineate the areas of the brain. A specialist with a thorough understanding of brain illnesses must manually identify the proper type of brain tumor. Additionally, processing a lot of images takes time and is tiresome. Therefore, automatic segmentation and classification techniques are required to speed up and enhance the diagnosis of brain tumors. Tumors can be quickly and safely detected by brain scans using imaging modalities, including computed tomography (CT), magnetic resonance imaging (MRI), and others. Machine learning (ML) and artificial intelligence (AI) have shown promise in the development of algorithms that aid in automatic classification and segmentation utilizing various imaging modalities. This review discussed various types of brain tumors, publicly accessible datasets, enhancement methods, segmentation, feature extraction, classification, machine learning techniques, and deep learning, learning through transfer, for the study of brain tumors.

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A Novel MRI Diagnosis Method for Brain Tumor Classification Based on CNN and Bayesian Optimization

Cited by 51 publications

References 39 publications

Isolated Convolutional-Neural-Network-Based Deep-Feature Extraction for Brain Tumor Classification Using Shallow Classifier

Isolated Convolutional-Neural-Network-Based Deep-Feature Extraction for Brain Tumor Classification Using Shallow Classifier

A CNN-Model to Classify Low-Grade and High-Grade Glioma From MRI Images

A Review of Brain Tumor Diagnosis and Segmentation

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