Deep Learning for Smart Healthcare—A Survey on Brain Tumor Detection from Medical Imaging

Arabahmadi, Mahsa; Farahbakhsh, Reza; Rezazadeh, Javad

doi:10.3390/s22051960

Cited by 96 publications

(45 citation statements)

References 121 publications

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“…Brain tumors are one of the most dreaded disorders in medical research. The term “brain tumor” refers to the uncontrolled and abnormal growth of cells within the brain that can affect the brain’s regulating mechanisms [ 1 , 2 ]. Brain tumors can expand and spread, putting pressure on the brain and negatively impacting physical health.…”

Section: Introductionmentioning

confidence: 99%

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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%

“…Therefore, the early identification and categorization of brain tumors is a vital study subject in the medical imaging domain. As a result, it aids in determining the best treatment choice to save a patient’s life [ 2 ]. Glioma, meningioma, and pituitary tumors are common brain tumor types.…”

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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“…Despite the growing interest in CNN-based CADx within the research community, translation into daily clinical practice has yet to be achieved due to obstacles such as the lack of an adequate amount of reliable data for training algorithms and imbalances within the datasets used for multi-class classification [ 32 , 33 ], among others. Several reviews [ 31 , 32 , 33 , 34 , 35 , 36 ] have been published in this regard, summarizing the classification methods and key achievements and pointing out some of the limitations in previous studies, but as of yet, none of them have focused on the deficiencies regarding clinical adoption or have attempted to determine the future research directions required to promote the application of deep learning models in clinical practice. For these reasons, the current review considers the key limitations and obstacles regarding the clinical applicability of studies in brain tumor classification using CNN algorithms and how to translate CNN-based CADx technology into better clinical decision making.…”

Section: Introductionmentioning

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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“…For example, in a brain tumor, the development of abnormal brain cells may damage the brain and may even threaten a patient’s life. Because brain tumors have long-term and life-altering physical and psychological implications, they can significantly influence a patient’s living quality and affect their entire life [ 2 ]. According to a World Health Organization (WHO) report [ 3 ], cancer is the second greatest cause of mortality globally.…”

Section: Introductionmentioning

confidence: 99%

Robust Gaussian and Nonlinear Hybrid Invariant Clustered Features Aided Approach for Speeded Brain Tumor Diagnosis

Almalki

Ali

Ahmed³

et al. 2022

Life

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Brain tumors reduce life expectancy due to the lack of a cure. Moreover, their diagnosis involves complex and costly procedures such as magnetic resonance imaging (MRI) and lengthy, careful examination to determine their severity. However, the timely diagnosis of brain tumors in their early stages may save a patient’s life. Therefore, this work utilizes MRI with a machine learning approach to diagnose brain tumor severity (glioma, meningioma, no tumor, and pituitary) in a timely manner. MRI Gaussian and nonlinear scale features are extracted due to their robustness over rotation, scaling, and noise issues, which are common in image processing features such as texture, local binary patterns, histograms of oriented gradient, etc. For the features, each MRI is broken down into multiple small 8 × 8-pixel MR images to capture small details. To counter memory issues, the strongest features based on variance are selected and segmented into 400 Gaussian and 400 nonlinear scale features, and these features are hybridized against each MRI. Finally, classical machine learning classifiers are utilized to check the performance of the proposed hybrid feature vector. An available online brain MRI image dataset is utilized to validate the proposed approach. The results show that the support vector machine-trained model has the highest classification accuracy of 95.33%, with a low computational time. The results are also compared with the recent literature, which shows that the proposed model can be helpful for clinicians/doctors for the early diagnosis of brain tumors.

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Deep Learning for Smart Healthcare—A Survey on Brain Tumor Detection from Medical Imaging

Cited by 96 publications

References 121 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

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

Robust Gaussian and Nonlinear Hybrid Invariant Clustered Features Aided Approach for Speeded Brain Tumor Diagnosis

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