Detection and Classification of Brain Hemorrhage Using Hounsfield Unit and Deep Learning Techniques

Phan, Anh-Cang; Cao, Hung-Phi; Trieu, Thanh-Ngoan; Phan, Thuong-Cang

doi:10.1007/978-981-33-4370-2_20

Cited by 9 publications

(3 citation statements)

References 8 publications

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“…Solorio-Ramírez et al [ 15 ] proposed a pattern classification technique, based on Minimalist Machine Learning (MML) implementation and a highly relevant feature selection method, so-called dMeans. Phan et al [ 16 ] developed a method-based DL and Hounsfield unit systems. It describes the duration and level of hemorrhage as well as classifies the brain hemorrhagic region on the MRI image.…”

Section: Related Workmentioning

confidence: 99%

Artificial Intelligence-Enabled Medical Analysis for Intracranial Cerebral Hemorrhage Detection and Classification

Meng

Wang

Zhang

et al. 2022

Journal of Healthcare Engineering

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Intracranial hemorrhage (ICH) becomes a crucial healthcare emergency, which requires earlier detection and accurate assessment. Owing to the increased death rate (around 40%), the earlier recognition and classification of disease using computed tomography (CT) images are necessary to ensure a favourable prediction and restrain the existence of neurologic deficits. Since the manual diagnosis approach is time-consuming, automated ICH detection and classification models using artificial intelligence (AI) models are required. With this motivation, this study introduces an AI-enabled medical analysis tool for ICH detection and classification (AIMA-ICHDC) using CT images. The proposed AIMA-ICHDC technique aims at identifying the presence of ICH and identifying the different grades. In addition, the AIMA-ICHDC technique involves the design of glowworm swarm optimization with fuzzy entropy clustering (GSO-FEC) technique for the segmentation process. Besides, the VGG-19 model was executed for generating a collection of feature vectors and the optimal mixed-kernel-based extreme learning machine (OMKELM) model is utilized as a classifier. To optimally select the weight parameter of the MKELM technique, the coyote optimization algorithm (COA) was utilized. A wide range of simulation analyses are carried out under varying aspects. As part of the AIMA-ICHDC method, ICH can be detected and graded using a single sample. For segmentation, the AIMA-ICHDC technique uses the GSO-FEC method, which is the design of glowworm swarm optimization (GSO). The comparative outcomes highlighted the betterment of the AIMA-ICHDC technique compared to the recent state-of-the-art ICH classification approaches in terms of several measures.

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Section: Related Workmentioning

confidence: 99%

Artificial Intelligence-Enabled Medical Analysis for Intracranial Cerebral Hemorrhage Detection and Classification

Meng

Wang

Zhang

et al. 2022

Journal of Healthcare Engineering

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“…Nonetheless, the voxel values cover a broad range while only a small portion of this spectrum is relevant for ICH segmentation. For example, hematoma usually lies in the range of 40 [HU] to 90 [HU] (Phan et al, 2020 ). Therefore, in all the following experiments, a window of [−50, 150] [HU] is used to adjust the contrast of the scans in view of focusing on the ICH signature while keeping variety in pixel values to let the networks understand and learn good features.…”

Section: Methodsmentioning

confidence: 99%

Label-efficient deep semantic segmentation of intracranial hemorrhages in CT-scans

Spahr,

Ståhle,

Wang

et al. 2023

Front. Neuroimaging

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Intracranial hemorrhage (ICH) is a common finding in traumatic brain injury (TBI) and computed tomography (CT) is considered the gold standard for diagnosis. Automated detection of ICH provides clinical value in diagnostics and in the ability to feed robust quantification measures into future prediction models. Several studies have explored ICH detection and segmentation but the research process is somewhat hindered due to a lack of open large and labeled datasets, making validation and comparison almost impossible. The complexity of the task is further challenged by the heterogeneity of ICH patterns, requiring a large number of labeled data to train robust and reliable models. Consequently, due to the labeling cost, there is a need for label-efficient algorithms that can exploit easily available unlabeled or weakly-labeled data. Our aims for this study were to evaluate whether transfer learning can improve ICH segmentation performance and to compare a variety of transfer learning approaches that harness unlabeled and weakly-labeled data. Three self-supervised and three weakly-supervised transfer learning approaches were explored. To be used in our comparisons, we also manually labeled a dataset of 51 CT scans. We demonstrate that transfer learning improves ICH segmentation performance on both datasets. Unlike most studies on ICH segmentation our work relies exclusively on publicly available datasets, allowing for easy comparison of performances in future studies. To further promote comparison between studies, we also present a new public dataset of ICH-labeled CT scans, Seq-CQ500.

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“…Afterward, to conduct the identification through CT brain images the proposed algorithm performance is examined and compared with k-nearest neighbors (KNN), multilayer perceptron (MLP), Naïve Bayes (NB), AdaBoost, random forests (RF), and support vector machine (SVM) classifiers. Phan et al [17] presented a new method based on the DL algorithm and hounsfield unit system. The proposed method not only describes the level and duration of hemorrhage but also classifies the brain hemorrhagic region on the MRI image.…”

mentioning

confidence: 99%

Deep convolutional neural network framework with multi-modal fusion for Alzheimer’s detection

Sharma,

Kaiser,

Ray

2024

IJRES

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The biomedical profession has gained importance due to the rapid and accurate diagnosis of clinical patients using computer-aided diagnosis (CAD) tools. The diagnosis and treatment of Alzheimer’s disease (AD) using complementary multimodalities can improve the quality of life and mental state of patients. In this study, we integrated a lightweight custom convolutional neural network (CNN) model and nature-inspired optimization techniques to enhance the performance, robustness, and stability of progress detection in AD. A multi-modal fusion database approach was implemented, including positron emission tomography (PET) and magnetic resonance imaging (MRI) datasets, to create a fused database. We compared the performance of custom and pre-trained deep learning models with and without optimization and found that employing natureinspired algorithms like the particle swarm optimization algorithm (PSO) algorithm significantly improved system performance. The proposed methodology, which includes a fused multimodality database and optimization strategy, improved performance metrics such as training, validation, test accuracy, precision, and recall. Furthermore, PSO was found to improve the performance of pre-trained models by 3-5% and custom models by up to 22%. Combining different medical imaging modalities improved the overall model performance by 2-5%. In conclusion, a customized lightweight CNN model and nature-inspired optimization techniques can significantly enhance progress detection, leading to better biomedical research and patient care.

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Detection and Classification of Brain Hemorrhage Using Hounsfield Unit and Deep Learning Techniques

Cited by 9 publications

References 8 publications

Artificial Intelligence-Enabled Medical Analysis for Intracranial Cerebral Hemorrhage Detection and Classification

Artificial Intelligence-Enabled Medical Analysis for Intracranial Cerebral Hemorrhage Detection and Classification

Label-efficient deep semantic segmentation of intracranial hemorrhages in CT-scans

Deep convolutional neural network framework with multi-modal fusion for Alzheimer’s detection

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