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

Xie, Yuting; Zaccagna, Fulvio; Rundo, Leonardo; Testa, Claudia; Agati, R.; Lodi, Raffaele; Manners, David Neil; Tonon, Caterina

doi:10.3390/diagnostics12081850

Cited by 83 publications

(55 citation statements)

References 121 publications

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“…Despite the fact that the biopsy is the reference standard for identifying the grade of gliomas, it is not favorable because of high invasiveness, expense, and its adverse effects such as bleeding and infection. Therefore, many researchers were motivated to investigate imaging techniques for a non-invasive, early, and precise grading of gliomas for a timely management plan [ 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 ]. In particular, magnetic resonance imaging (MRI) is the most common imaging modality for the diagnosis and assessment of cerebral neoplasms, including gliomas.…”

Section: Introductionmentioning

confidence: 99%

A Novel System for Precise Grading of Glioma

et al. 2022

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Gliomas are the most common type of primary brain tumors and one of the highest causes of mortality worldwide. Accurate grading of gliomas is of immense importance to administer proper treatment plans. In this paper, we develop a comprehensive non-invasive multimodal magnetic resonance (MR)-based computer-aided diagnostic (CAD) system to precisely differentiate between different grades of gliomas (Grades: I, II, III, and IV). A total of 99 patients with gliomas (M = 49, F = 50, age range = 1–79 years) were included after providing their informed consent to participate in this study. The proposed imaging-based glioma grading (GG-CAD) system utilizes three different MR imaging modalities, namely; contrast-enhanced T1-MR, T2-MR known as fluid-attenuated inversion-recovery (FLAIR), and diffusion-weighted (DW-MR) to extract the following imaging features: (i) morphological features based on constructing the histogram of oriented gradients (HOG) and estimating the glioma volume, (ii) first and second orders textural features by constructing histogram, gray-level run length matrix (GLRLM), and gray-level co-occurrence matrix (GLCM), (iii) functional features by estimating voxel-wise apparent diffusion coefficients (ADC) and contrast-enhancement slope. These features are then integrated together and processed using a Gini impurity-based selection approach to find the optimal set of significant features. The reduced significant features are then fed to a multi-layer perceptron artificial neural networks (MLP-ANN) classification model to obtain the final diagnosis of a glioma tumor as Grade I, II, III, or IV. The GG-CAD system was evaluated on the enrolled 99 gliomas (Grade I = 13, Grade II = 22, Grade III = 22, and Grade IV = 42) using a leave-one-subject-out (LOSO) and k-fold stratified (with k = 5 and 10) cross-validation approach. The GG-CAD achieved 0.96 ± 0.02 quadratic-weighted Cohen’s kappa and 95.8% ± 1.9% overall diagnostic accuracy at LOSO and an outstanding diagnostic performance at k = 10 and 5. Alternative classifiers, including RFs and SVMlin produced inferior results compared to the proposed MLP-ANN GG-CAD system. These findings demonstrate the feasibility of the proposed CAD system as a novel tool to objectively characterize gliomas using the comprehensive extracted and selected imaging features. The developed GG-CAD system holds promise to be used as a non-invasive diagnostic tool for Precise Grading of Glioma.

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Section: Introductionmentioning

confidence: 99%

A Novel System for Precise Grading of Glioma

et al. 2022

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“…Owing to the rapid development of deep learning models, artificial intelligence based on deep learning has dominated various fields [ 1 , 2 , 3 ]. Deep learning models are being used extensively in diagnostics [ 4 , 5 , 6 ], medical imaging [ 7 , 8 , 9 , 10 ], and genome sequencing [ 11 , 12 , 13 ]. Additionally, the prognosis of various diseases can be estimated by deep learning models [ 14 , 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

Estimating the Prognosis of Low-Grade Glioma with Gene Attention Using Multi-Omics and Multi-Modal Schemes

Choi

Lee

2022

Biology

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The prognosis estimation of low-grade glioma (LGG) patients with deep learning models using gene expression data has been extensively studied in recent years. However, the deep learning models used in these studies do not utilize the latest deep learning techniques, such as residual learning and ensemble learning. To address this limitation, in this study, a deep learning model using multi-omics and multi-modal schemes, namely the Multi-Prognosis Estimation Network (Multi-PEN), is proposed. When using Multi-PEN, gene attention layers are employed for each datatype, including mRNA and miRNA, thereby allowing us to identify prognostic genes. Additionally, recent developments in deep learning, such as residual learning and layer normalization, are utilized. As a result, Multi-PEN demonstrates competitive performance compared to conventional models for prognosis estimation. Furthermore, the most significant prognostic mRNA and miRNA were identified using the attention layers in Multi-PEN. For instance, MYBL1 was identified as the most significant prognostic mRNA. Such a result accords with the findings in existing studies that have demonstrated that MYBL1 regulates cell survival, proliferation, and differentiation. Additionally, hsa-mir-421 was identified as the most significant prognostic miRNA, and it has been extensively reported that hsa-mir-421 is highly associated with various cancers. These results indicate that the estimations of Multi-PEN are valid and reliable and showcase Multi-PEN’s capacity to present hypotheses regarding prognostic mRNAs and miRNAs.

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“…In [ 13 ], a multi-model CNN based hybrid approach is proposed for the classification of brain MR images. Similarly, several recent studies are discussed in [ 14 ] which utilizes different CNN models for brain tumor classification.…”

Section: Introductionmentioning

confidence: 99%

DeepTumor: Framework for Brain MR Image Classification, Segmentation and Tumor Detection

Latif

2022

Diagnostics

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The proper segmentation of the brain tumor from the image is important for both patients and medical personnel due to the sensitivity of the human brain. Operation intervention would require doctors to be extremely cautious and precise to target the brain’s required portion. Furthermore, the segmentation process is also important for multi-class tumor classification. This work primarily concentrated on making a contribution in three main areas of brain MR Image processing for classification and segmentation which are: Brain MR image classification, tumor region segmentation and tumor classification. A framework named DeepTumor is presented for the multistage-multiclass Glioma Tumor classification into four classes; Edema, Necrosis, Enhancing and Non-enhancing. For the brain MR image binary classification (Tumorous and Non-tumorous), two deep Convolutional Neural Network) CNN models were proposed for brain MR image classification; 9-layer model with a total of 217,954 trainable parameters and an improved 10-layer model with a total of 80,243 trainable parameters. In the second stage, an enhanced Fuzzy C-means (FCM) based technique is proposed for the tumor segmentation in brain MR images. In the final stage, an enhanced CNN model 3 with 11 hidden layers and a total of 241,624 trainable parameters was proposed for the classification of the segmented tumor region into four Glioma Tumor classes. The experiments are performed using the BraTS MRI dataset. The experimental results of the proposed CNN models for binary classification and multiclass tumor classification are compared with the existing CNN models such as LeNet, AlexNet and GoogleNet as well as with the latest literature.

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

Cited by 83 publications

References 121 publications

A Novel System for Precise Grading of Glioma

A Novel System for Precise Grading of Glioma

Estimating the Prognosis of Low-Grade Glioma with Gene Attention Using Multi-Omics and Multi-Modal Schemes

DeepTumor: Framework for Brain MR Image Classification, Segmentation and Tumor Detection

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