Automatic Identification of Intracranial Hemorrhage on CT/MRI Image Using Meta-Architectures Improved from Region-Based CNN

Le, Thi-Hoang-Yen; Phan, Anh-Cang; Cao, Hung-Phi; Phan, Thuong-Cang

doi:10.1007/978-3-030-21803-4_74

Cited by 9 publications

(9 citation statements)

References 8 publications

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“…ResNet101 is named after 101 layers of Residual Network and 50 layer ResNet architecture is a modified version of ResNet101 architecture 96,97 . In 2016, the ResNet model was originally proposed by He et al 98 ResNet is an abbreviation for Residual Networks and has been employed in solving numerous problems related to computer vision and its other applications.…”

Section: Methodsmentioning

confidence: 99%

“…ResNet101 is named after 101 layers of Residual Network and 50 layer ResNet architecture is a modified version of ResNet101 architecture. 96,97 In 2016, the ResNet model was originally proposed by He et al 98 ResNet is an abbreviation for Residual Networks and has been employed in solving numerous problems related to computer vision and its other applications. ResNet is one of the deepest Convolutional Neural Network architectures used on large scales and has a wide range of applications for ImageNet (i.e., object detection and recognition, different classification purposes).…”

Section: Resnet101mentioning

confidence: 99%

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Detecting brain tumors using deep learning convolutional neural network with transfer learning approach

Anjum

Hussain

Ali

et al. 2021

Int J Imaging Syst Tech

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Accurate classification of brain tumor subtypes is important for prognosis and treatment. In this study, we optimized and applied non-deep learning methods based on hand-crafted features and deep learning methods based on transfer learning using softmax as classification and KNN and SVM as classification for features extracted from deep features of ResNet101. For non-deep learning techniques, we extracted multimodal features as input to machine learning classifiers. For convolutional neural networks, we optimized and applied GoogleNet and ResNet101with transfer learning approach. The performance was evaluated in terms of sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), false positive rate (FPR), total accuracy (TA), and area under the receiver operating curve (AUC) using Jack-knife 10-fold cross validation (CV) for the testing and validation of the dataset. For two-class classification, entropy features using SVM Gaussian yielded the highest performance with 93.84% TA and 0.9874 AUC, and GoogleNet yielded 99.33% TA. For Multiclass classification, the highest performance to detect pituitary tumor yielded 95.65% accuracy and 0.95 AUC using ResNet101 with transfer learning. Deep features from ResNet101 using KNN improved detection of pituitary tumor (98.80% accuracy, 0.99 AUC), glioma (93.47% accuracy, 0.93 AUC), and meningioma (93.36% accuracy, 0.89 AUC). The deep features ResNet101-SVM to detect pituitary tumor yielded performance (98.

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Section: Methodsmentioning

confidence: 99%

Section: Resnet101mentioning

confidence: 99%

Detecting brain tumors using deep learning convolutional neural network with transfer learning approach

Anjum

Hussain

Ali

et al. 2021

Int J Imaging Syst Tech

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“…Named after its 101 layers, ResNet101 is a modified version of the ResNet architecture, which originally introduced the concept of residual connections. 67,68 ResNet, an abbreviation for Residual Networks, is a deep CNN architecture with a wide range of applications in computer vision, including image classification, object detection, and recognition. 70 The core idea behind ResNet is the use of residual connections, which allow gradients to flow more effectively through the network, preventing the vanishing gradient problem that can plague deep neural networks.…”

Section: Resnet101mentioning

confidence: 99%

Deep learning convolutional neural network ResNet101 and radiomic features accurately analyzes mpMRI imaging to predict MGMT promoter methylation status with transfer learning approach

Shim,

Hussain,

Aziz

et al. 2024

Int J Imaging Syst Tech

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Accurate brain tumor classification is crucial for enhancing the diagnosis, prognosis, and treatment of glioblastoma patients. We employed the ResNet101 deep learning method with transfer learning to analyze the 2021 Radiological Society of North America (RSNA) Brain Tumor challenge dataset. This dataset comprises four structural magnetic resonance imaging (MRI) sequences: fluid‐attenuated inversion‐recovery (FLAIR), T1‐weighted pre‐contrast (T1w), T1‐weighted post‐contrast (T1Gd), and T2‐weighted (T2). We assessed the model's performance using standard evaluation metrics. The highest performance to detect MGMT methylation status for patients suffering glioblastoma was an accuracy (85.48%), sensitivity (80.64%), specificity (90.32%). Whereas classification performance with no tumor was yielded with accuracy (85.48%), sensitivity (90.32%), specificity (80.64%). The radiomic features (74) computed with ensembled Bagged Tree and relief feature selection method (30/74) improved the validation accuracy of 84.3% and AUC of 0.9038 to detect. O6‐methylguanine‐DNA methyltransferase (MGMT) promoter methylation status in glioblastoma patients holds promise for optimizing treatment planning and prognosis. By understanding MGMT methylation status, clinicians can make informed decisions about treatment strategies, potentially leading to improved clinical outcomes.

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“…Previously, Al Okashi et al ( 127 ) proposed an ensemble learning system for hemorrhage detection on brain MRI, but describes head CT images throughout the paper as brain MRI images. Le et al ( 126 ) proposed R-FCN as a classification model for CT/MRI images to differentiate between different hemorrhagic subtypes. However, their methods and figures also use CT scans, such that the relevance to MRI is unclear.…”

Section: Intracranial Hemorrhage Detectionmentioning

confidence: 99%

“…MRI's current limited role in acute TBI also limits the quantity of available training data. Despite this, a small number of MRI hemorrhage detection algorithms have been proposed and are briefly discussed below for completeness (124)(125)(126)(127).…”

Section: Intracranial Hemorrhage Detectionmentioning

confidence: 99%

Computational Approaches for Acute Traumatic Brain Injury Image Recognition

Lin

Yuh

2022

Front. Neurol.

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In recent years, there have been major advances in deep learning algorithms for image recognition in traumatic brain injury (TBI). Interest in this area has increased due to the potential for greater objectivity, reduced interpretation times and, ultimately, higher accuracy. Triage algorithms that can re-order radiological reading queues have been developed, using classification to prioritize exams with suspected critical findings. Localization models move a step further to capture more granular information such as the location and, in some cases, size and subtype, of intracranial hematomas that could aid in neurosurgical management decisions. In addition to the potential to improve the clinical management of TBI patients, the use of algorithms for the interpretation of medical images may play a transformative role in enabling the integration of medical images into precision medicine. Acute TBI is one practical example that can illustrate the application of deep learning to medical imaging. This review provides an overview of computational approaches that have been proposed for the detection and characterization of acute TBI imaging abnormalities, including intracranial hemorrhage, skull fractures, intracranial mass effect, and stroke.

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Automatic Identification of Intracranial Hemorrhage on CT/MRI Image Using Meta-Architectures Improved from Region-Based CNN

Cited by 9 publications

References 8 publications

Detecting brain tumors using deep learning convolutional neural network with transfer learning approach

Detecting brain tumors using deep learning convolutional neural network with transfer learning approach

Deep learning convolutional neural network ResNet101 and radiomic features accurately analyzes mpMRI imaging to predict MGMT promoter methylation status with transfer learning approach

Computational Approaches for Acute Traumatic Brain Injury Image Recognition

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