Computer Aided Diagnosis System-A Decision Support System for Clinical Diagnosis of Brain Tumours

Tiwari, Puneet; Sachdeva, Jainy; Ahuja, Chirag Kamal; Khandelwal, Niranjan

doi:10.2991/ijcis.2017.10.1.8

Cited by 14 publications

(6 citation statements)

References 22 publications

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“…The feature extraction approaches [12, [138][139][140] including GLCM [15,141,142], geometrical features (area, perimeter, and circularity) [15], first-order statistical (FOS), GWT [143,144], Hu moment invariants (HMI) [145], multifractal features [146], 3D Haralick features [147], LBP [148], GWT [11], HOG [14, 137], texture and shape [82, 143,149,150], co-occurrence matrix, gradient, run-length matrix [151], SFTA, curvature features [152,153], Gabor like multiscale texton features [154], Gabor wavelet and statistical features [142,143] are utilized for classification. Table 3 lists the summary of feature extraction methods.…”

Section: Feature Extraction Methodsmentioning

confidence: 99%

Brain tumor detection and classification using machine learning: a comprehensive survey

Amin

Sharif

Haldorai³

et al. 2021

Complex Intell. Syst.

208

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Brain tumor occurs owing to uncontrolled and rapid growth of cells. If not treated at an initial phase, it may lead to death. Despite many significant efforts and promising outcomes in this domain, accurate segmentation and classification remain a challenging task. A major challenge for brain tumor detection arises from the variations in tumor location, shape, and size. The objective of this survey is to deliver a comprehensive literature on brain tumor detection through magnetic resonance imaging to help the researchers. This survey covered the anatomy of brain tumors, publicly available datasets, enhancement techniques, segmentation, feature extraction, classification, and deep learning, transfer learning and quantum machine learning for brain tumors analysis. Finally, this survey provides all important literature for the detection of brain tumors with their advantages, limitations, developments, and future trends.

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Section: Feature Extraction Methodsmentioning

confidence: 99%

Brain tumor detection and classification using machine learning: a comprehensive survey

Amin

Sharif

Haldorai³

et al. 2021

Complex Intell. Syst.

208

View full text Add to dashboard Cite

show abstract

“…A suitable CAD approach to classifying brain tumors is proposed in [92]. The database includes Meningioma, Astrocytoma, and Normal brain areas along with primary brain tumors.…”

Section: Mri Brain Tumor Segmentationmentioning

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 suitable CAD approach toward classifying brain tumors is proposed in [ 93 ]. The database includes meningioma, astrocytoma, normal brain areas, and primary brain tumors.…”

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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Computer Aided Diagnosis System-A Decision Support System for Clinical Diagnosis of Brain Tumours

Cited by 14 publications

References 22 publications

Brain tumor detection and classification using machine learning: a comprehensive survey

Brain tumor detection and classification using machine learning: a comprehensive survey

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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