Brain tumor detection and multi‐classification using advanced deep learning techniques

Sadad, Tariq; Rehman, Amjad; Munir, Asim; Tariq, Usman; Noor, Ayesha; Abbasi, Rashid

doi:10.1002/jemt.23688

Cited by 146 publications

(53 citation statements)

References 79 publications

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“…Their model produced an accuracy of 97% and can be deployed on mobile devices. Sadad et al (2021) implemented brain tumor segmentation based on U-Net and ResNet-50. They proposed to use transfer learning with pre-trained CNN models for brain tumor classification.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: PBTNet: A New Computer-Aided Diagnosis System for Detecting Primary Brain Tumors

Satapathy

Wang

et al. 2021

Front. Cell Dev. Biol.

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Brain tumors are among the leading human killers. There are over 120 different types of brain tumors, but they mainly fall into two groups: primary brain tumors and metastatic brain tumors. Primary brain tumors develop from normal brain cells. Early and accurate detection of primary brain tumors is vital for the treatment of this disease. Magnetic resonance imaging is the most common method to diagnose brain diseases, but the manual interpretation of the images suffers from high inter-observer variance. In this paper, we presented a new computer-aided diagnosis system named PBTNet for detecting primary brain tumors in magnetic resonance images. A pre-trained ResNet-18 was selected as the backbone model in our PBTNet, but it was fine-tuned only for feature extraction. Then, three randomized neural networks, Schmidt neural network, random vector functional-link, and extreme learning machine served as the classifiers in the PBTNet, which were trained with the features and their labels. The final predictions of the PBTNet were generated by the ensemble of the outputs from the three classifiers. 5-fold cross-validation was employed to evaluate the classification performance of the PBTNet, and experimental results demonstrated that the proposed PBTNet was an effective tool for the diagnosis of primary brain tumors.

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Section: Introductionmentioning

confidence: 99%

RETRACTED: PBTNet: A New Computer-Aided Diagnosis System for Detecting Primary Brain Tumors

Satapathy

Wang

et al. 2021

Front. Cell Dev. Biol.

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“…MLP and adaptive network-based fuzzy inference (ANFIS) are used in the estimation and forecasting of dynamic variance behaviors. It was proposed to take the verified cases and estimate the numbers of infected persons in the country with the hybrid approach to vector control by Support Vector Regression (SVR) and ARIMA (Al-Ameen et al, 2015;Sadad et al, 2021). Also, Parbat and Chakraborty (2020) used the RBF kernel model for forecasting everyday cases, recovered conditions, and death.…”

mentioning

confidence: 99%

Machine learning techniques to detect and forecast the daily total COVID‐19 infected and deaths cases under different lockdown types

Saba

Abunadi

Shahzad

et al. 2021

Microscopy Res & Technique

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COVID‐19 has impacted the world in many ways, including loss of lives, economic downturn and social isolation. COVID‐19 was emerged due to the SARS‐CoV‐2 that is highly infectious pandemic. Every country tried to control the COVID‐19 spread by imposing different types of lockdowns. Therefore, there is an urgent need to forecast the daily confirmed infected cases and deaths in different types of lockdown to select the most appropriate lockdown strategies to control the intensity of this pandemic and reduce the burden in hospitals. Currently are imposed three types of lockdown (partial, herd, complete) in different countries. In this study, three countries from every type of lockdown were studied by applying time‐series and machine learning models, named as random forests, K‐nearest neighbors, SVM, decision trees (DTs), polynomial regression, Holt winter, ARIMA, and SARIMA to forecast daily confirm infected cases and deaths due to COVID‐19. The models' accuracy and effectiveness were evaluated by error based on three performance criteria. Actually, a single forecasting model could not capture all data sets' trends due to the varying nature of data sets and lockdown types. Three top‐ranked models were used to predict the confirmed infected cases and deaths, the outperformed models were also adopted for the out‐of‐sample prediction and obtained very close results to the actual values of cumulative infected cases and deaths due to COVID‐19. This study has proposed the auspicious models for forecasting and the best lockdown strategy to mitigate the causalities of COVID‐19.

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“…The method consists of the three major steps such as pre-processing, augmentation of data, and segmentation and classification using transfer learning models. In which ResNet-50, DenseNet-201, MobileNet-v2 and Inceptionv3 are utilized to classify the brain lesions with 0.95 IoU [237].…”

Section: Brain Tumor Detection Using Transfer Learningmentioning

confidence: 99%

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

Amin

Sharif

Haldorai³

et al. 2021

Complex Intell. Syst.

173

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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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Brain tumor detection and multi‐classification using advanced deep learning techniques

Cited by 146 publications

References 79 publications

RETRACTED: PBTNet: A New Computer-Aided Diagnosis System for Detecting Primary Brain Tumors

RETRACTED: PBTNet: A New Computer-Aided Diagnosis System for Detecting Primary Brain Tumors

Machine learning techniques to detect and forecast the daily total COVID‐19 infected and deaths cases under different lockdown types

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

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