Brain Tumor Classification Based on Hybrid Optimized Multi-features Analysis Using Magnetic Resonance Imaging Dataset

Nawaz, Syed Ali; Khan, Dost Muhammad; Qadri, Salman

doi:10.1080/08839514.2022.2031824

Cited by 38 publications

(15 citation statements)

References 51 publications

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“…We initiate our analysis by extracting the base results of all feature classes without applying feature selection methods. Please note that we adopt five fold cross validation approach to test all models by varying training and testing data distribution ( Refaeilzadeh, Tang & Liu, 2009 ; Nawaz, Khan & Qadri, 2022 ; Chatterjee et al, 2022 ; Xu et al, 2021 ). Tables 6 and 7 shows the result of BraTS dataset for classification and regression models, respectively.…”

Section: Resultsmentioning

confidence: 99%

Towards survival prediction of cancer patients using medical images

Haq

Tahir

Firdous

et al. 2022

PeerJ Computer Science

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Survival prediction of a patient is a critical task in clinical medicine for physicians and patients to make an informed decision. Several survival and risk scoring methods have been developed to estimate the survival score of patients using clinical information. For instance, the Global Registry of Acute Coronary Events (GRACE) and Thrombolysis in Myocardial Infarction (TIMI) risk scores are developed for the survival prediction of heart patients. Recently, state-of-the-art medical imaging and analysis techniques have paved the way for survival prediction of cancer patients by understanding key features extracted from Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scanned images with the help of image processing and machine learning techniques. However, survival prediction is a challenging task due to the complexity in benchmarking of image features, feature selection methods, and machine learning models. In this article, we evaluate the performance of 156 visual features from radiomic and hand-crafted feature classes, six feature selection methods, and 10 machine learning models to benchmark their performance. In addition, MRI scanned Brain Tumor Segmentation (BraTS) and CT scanned non-small cell lung cancer (NSCLC) datasets are used to train classification and regression models. Our results highlight that logistic regression outperforms for the classification with 66 and 54% accuracy for BraTS and NSCLC datasets, respectively. Moreover, our analysis of best-performing features shows that age is a common and significant feature for survival prediction. Also, gray level and shape-based features play a vital role in regression. We believe that the study can be helpful for oncologists, radiologists, and medical imaging researchers to understand and automate the procedure of decision-making and prognosis of cancer patients.

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Section: Resultsmentioning

confidence: 99%

Towards survival prediction of cancer patients using medical images

Haq

Tahir

Firdous

et al. 2022

PeerJ Computer Science

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“…The effective hybrid-brain-tumor classification (HBTC) is developed by Nawaz et al [19] for multiple classifications of brain tumors. The proposed model classifies metastatic (meta), meningioma (menin), glioma, and cystic (cyst) brain tumors in this research.…”

Section: Related Prior Researchmentioning

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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“…Segmentation. An enhanced EDPSO (Darwinian particle swarm optimization) and Quantum Entanglement and Wormhole Behaved Particle Swarm Optimization Techniques [12][13][14] were proposed to segment a tumour image which overcomes this existing method of GCPSO (Guaranteed Convergence Particle Swarm Optimization). We propose, a new Hybrid GCPSO (Guaranteed Convergence Particle Swarm Optimization)-FCM (Fuzzy C-Mean) algorithm use to each particle to every number of generations/iterations so the ftness value of all the particles to improve.…”

Section: Hybridmentioning

confidence: 99%

A Novel Approach for Hybrid Image Segmentation GCPSO: FCM Techniques for MRI Brain Tumour Identification and Classification

Kavitha

Jayagopal

Kumar

et al. 2022

Computational Intelligence and Neuroscience

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In recent times, the early detection of brain tumour analysis and classification has become a very vital part of the medical field. The MRI scan image is the most significant tool to study brain tissue for proper diagnosis and efficient treatment planning to detect the early stages. In this research study, the two contributions were executed in the preprocessing mode. (a) Using wavelet transform to apply decomposed sub-bands of a low-frequency signal to control and adapt the spatial and intensity parameters in a bilateral filter and (b) to detect texture regions and block boundary to control and adapt the spatial and intensity parameters in a bilateral filter When compared to other image resolution methods, the adaptive bilateral method restores the original image quality and has a higher accuracy rate. Using the hybrid segmentation method of GCPSO (Guaranteed Convergence Particle Swarm Optimization) -FCM (Fuzzy C-Mean) techniques, the results were compared with various segmentation. The proposed segmentation gives a better accuracy rate of 95.32%.

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Brain Tumor Classification Based on Hybrid Optimized Multi-features Analysis Using Magnetic Resonance Imaging Dataset

Cited by 38 publications

References 51 publications

Towards survival prediction of cancer patients using medical images

Towards survival prediction of cancer patients using medical images

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

A Novel Approach for Hybrid Image Segmentation GCPSO: FCM Techniques for MRI Brain Tumour Identification and Classification

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