Three‐class brain tumor classification from magnetic resonance images using separable convolution based neural network

Isunuri, Bala Venkateswarlu; Kakarla, Jagadeesh

doi:10.1002/cpe.6541

Cited by 21 publications

(7 citation statements)

References 21 publications

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“…332 (259 HGG, 73 LGG), 2. 259 Normal x x x x Affine Khazaee et al [ 133 ] 2022 BraTS2019 ceT 1 w, T 2 w, FLAIR 335 (259 HGG, 76 LGG) 26,904 (13,233 HGG, 13,671 LGG) x x Isunuri et al [ 134 ] 2022 Figshare (Cheng et al, 2017) ceT 1 w 233 (as shown in Table 2 ) 3064 (as shown in Table 2 ) x x ...…”

Section: Resultsmentioning

confidence: 99%

Convolutional Neural Network Techniques for Brain Tumor Classification (from 2015 to 2022): Review, Challenges, and Future Perspectives

et al. 2022

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Convolutional neural networks (CNNs) constitute a widely used deep learning approach that has frequently been applied to the problem of brain tumor diagnosis. Such techniques still face some critical challenges in moving towards clinic application. The main objective of this work is to present a comprehensive review of studies using CNN architectures to classify brain tumors using MR images with the aim of identifying useful strategies for and possible impediments in the development of this technology. Relevant articles were identified using a predefined, systematic procedure. For each article, data were extracted regarding training data, target problems, the network architecture, validation methods, and the reported quantitative performance criteria. The clinical relevance of the studies was then evaluated to identify limitations by considering the merits of convolutional neural networks and the remaining challenges that need to be solved to promote the clinical application and development of CNN algorithms. Finally, possible directions for future research are discussed for researchers in the biomedical and machine learning communities. A total of 83 studies were identified and reviewed. They differed in terms of the precise classification problem targeted and the strategies used to construct and train the chosen CNN. Consequently, the reported performance varied widely, with accuracies of 91.63–100% in differentiating meningiomas, gliomas, and pituitary tumors (26 articles) and of 60.0–99.46% in distinguishing low-grade from high-grade gliomas (13 articles). The review provides a survey of the state of the art in CNN-based deep learning methods for brain tumor classification. Many networks demonstrated good performance, and it is not evident that any specific methodological choice greatly outperforms the alternatives, especially given the inconsistencies in the reporting of validation methods, performance metrics, and training data encountered. Few studies have focused on clinical usability.

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Section: Resultsmentioning

confidence: 99%

Convolutional Neural Network Techniques for Brain Tumor Classification (from 2015 to 2022): Review, Challenges, and Future Perspectives

et al. 2022

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“…For the two datasets, the suggested method has accuracy of 99.8% and 97.14%. A seven-layer CNN was suggested in [110] to assist with the three-class categorization of brain MR images. To decrease computing time, separable convolution was used.…”

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 seven-layer CNN was suggested in [ 111 ] to assist with the three-class categorization of brain MR images. To decrease computing time, separable convolution was used.…”

Section: Literature Reviewmentioning

confidence: 99%

A Review of Recent Advances in Brain Tumor Diagnosis Based on AI-Based Classification

Kaifi

2023

Diagnostics

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Uncontrolled and fast cell proliferation is the cause of brain tumors. Early cancer detection is vitally important 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. Brain tumor segmentation aims to delineate accurately the areas of brain tumors. A specialist with a thorough understanding of brain illnesses is needed to manually identify the proper type of brain tumor. Additionally, processing many 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 developing algorithms that aid in automatic classification and segmentation utilizing various imaging modalities. The right segmentation method must be used to precisely classify patients with brain tumors to enhance diagnosis and treatment. This review describes multiple types of brain tumors, publicly accessible datasets, enhancement methods, segmentation, feature extraction, classification, machine learning techniques, deep learning, and learning through a transfer to study brain tumors. In this study, we attempted to synthesize brain cancer imaging modalities with automatically computer-assisted methodologies for brain cancer characterization in ML and DL frameworks. Finding the current problems with the engineering methodologies currently in use and predicting a future paradigm are other goals of this article.

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Three‐class brain tumor classification from magnetic resonance images using separable convolution based neural network

Cited by 21 publications

References 21 publications

Convolutional Neural Network Techniques for Brain Tumor Classification (from 2015 to 2022): Review, Challenges, and Future Perspectives

Convolutional Neural Network Techniques for Brain Tumor Classification (from 2015 to 2022): Review, Challenges, and Future Perspectives

A Review of Brain Tumor Diagnosis and Segmentation

A Review of Recent Advances in Brain Tumor Diagnosis Based on AI-Based Classification

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