Intelligent brain hemorrhage diagnosis using artificial neural networks

Balasooriya, Ushani; Perera, M.U.S.

doi:10.1109/beiac.2012.6226036

Cited by 23 publications

(6 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Intracranial haemorrhage (ICH) reveals as a bleeding within the intracranial vault [1]. Weak blood vessels, hypertension, trauma, and drug abuse are generally what trigger such a medical condition.…”

Section: Introductionmentioning

confidence: 99%

Application of Deep Learning in Neuroradiology: Brain Haemorrhage Classification Using Transfer Learning

Dawud

Yurtkan

Öztoprak

2019

Computational Intelligence and Neuroscience

118

View full text Add to dashboard Cite

In this paper, we address the problem of identifying brain haemorrhage which is considered as a tedious task for radiologists, especially in the early stages of the haemorrhage. The problem is solved using a deep learning approach where a convolutional neural network (CNN), the well-known AlexNet neural network, and also a modified novel version of AlexNet with support vector machine (AlexNet-SVM) classifier are trained to classify the brain computer tomography (CT) images into haemorrhage or nonhaemorrhage images. The aim of employing the deep learning model is to address the primary question in medical image analysis and classification: can a sufficient fine-tuning of a pretrained model (transfer learning) eliminate the need of building a CNN from scratch? Moreover, this study also aims to investigate the advantages of using SVM as a classifier instead of a three-layer neural network. We apply the same classification task to three deep networks; one is created from scratch, another is a pretrained model that was fine-tuned to the brain CT haemorrhage classification task, and our modified novel AlexNet model which uses the SVM classifier. The three networks were trained using the same number of brain CT images available. The experiments show that the transfer of knowledge from natural images to medical images classification is possible. In addition, our results proved that the proposed modified pretrained model “AlexNet-SVM” can outperform a convolutional neural network created from scratch and the original AlexNet in identifying the brain haemorrhage.

show abstract

Section: Introductionmentioning

confidence: 99%

Application of Deep Learning in Neuroradiology: Brain Haemorrhage Classification Using Transfer Learning

Dawud

Yurtkan

Öztoprak

2019

Computational Intelligence and Neuroscience

118

View full text Add to dashboard Cite

show abstract

“…Intracranial hemorrhage is one of the common causes of morbidity and mortality in the world [27]. It is a medical emergency and needs to be recognized and treated urgently.…”

Section: Discussionmentioning

confidence: 99%

Intracranial Hemorrhage Detection in Ct Scan Using Deep Learning

Tharek¹,

Muda²,

Hudi³

et al. 2022

AJMedTech

View full text Add to dashboard Cite

Missed detection of intracranial hemorrhage in Head CT scans has significantly impacted patient morbidity and mortality. Early detection of intracranial hemorrhage enables patients to receive appropriate treatment which resulted in a better outcome. Some doctors have limited experience in interpreting the CT scan hence increasing the probability to miss the hemorrhage. The main objective of this study is to develop an algorithm model capable of detecting intracranial hemorrhage in a head CT scan. We are using deep learning from a convolutional neural network (CNN) to produce this algorithm module. This was a cross-sectional study using secondary data, in which 200 data were collected from public datasets. All of the samples have been anonymized into secondary data. The algorithm model is trained using deep learning via a Jupyter Notebook platform. To analyze the algorithm model performance, a confusion matrix was used to measure the accuracy, sensitivity, specificity, precision, and F1 score. This study showed that from 200 training data, 95 samples were true positive, 95 samples were true negative, 7 samples were false positive, and 3 samples were false negative. This algorithm model shows high sensitivity (0.9694), high specificity (0.9314), high precision (0.9314), and high accuracy (0.9500) with an F1 score of 0.9500. This study has proven that deep learning by using CNN enables us to create an accurate classifier that can differentiate between head CT scan with intracranial hemorrhage and without hemorrhage. The main objective of this study is to develop an algorithm model capable of detecting intracranial hemorrhage in a head CT scan. We are using deep learning from a convolutional neural network (CNN) to produce this algorithm module. This was a cross-sectional study using secondary data, in which 200 data were collected from public datasets. This dataset is owned by Abdul Kader Helwan, an academic staff at Al-Manar University of Tripoli, Lebanon. Permission to use the dataset for this research was officially obtained from the owner. All of samples have been anonymized into secondary data. The data is divided into train, validation, and test samples. The algorithm model is trained using deep learning via a Jupyter Notebook platform. To analyze the algorithm model performance, confusion matrix was used to measure the accuracy, sensitivity, specificity, precision, and F1 score. This study showed that from 200 training data, 95 samples were true positive, 95 samples were true negative, 7 samples were false positive, and 3 samples were false negative. This algorithm model shows high sensitivity (0.9694), high specificity (0.9314), high precision (0.9314), and high accuracy (0.9500) with F1 score of 0.9500. Hence, this study has proven that deep learning by using CNN enables us to create an accurate classifier that can differentiate between head CT scan with intracranial hemorrhage and without hemorrhage.

show abstract

“…3,4 ICH is classified into five subtypes based on the location of bleeding, namely, cerebral parenchyma hemorrhage (CPH), epidural hemorrhage (EDH), subdural hemorrhage (SDH) intraventricular hemorrhage (IVH), and subarachnoid hemorrhage (SAH). 5,6 On the basis of the anatomy of brain structure, the term CPH refers to bleeding within the parenchyma of the brain. SAH means bleeding between the pia and arachnoid membranes.…”

Section: Introductionmentioning

confidence: 99%

An efficient stacked bidirectional GRU‐LSTM network for intracranial hemorrhage detection

Kothala,

Guntur

2023

Int J Imaging Syst Tech

View full text Add to dashboard Cite

Intracranial hemorrhage (ICH) is a dangerous condition that needs prompt diagnosis and treatment. Computed tomography (CT) images are employed in examination of individuals with ICH, which produces better results and cost‐effective than MRI. The existing convolutional neural network (CNN) models are unable to consider inter‐pixel dependency, which leads to false predictions while considering the input CT Images. In this study, we implemented an efficient model of a stack of bidirectional gated recurrent unit (Bi‐GRU) with a bidirectional long short‐term memory (Bi‐LSTM) based CNN to improve detection accuracy in the case of 2D slices. The proposed model holds slice‐wise information by accessing the properties of both Bi‐LSTM and Bi‐GRU modules in a single unit. As a result, the model attained a testing and training accuracy of 96.2% and 93.4%, respectively, with a test loss score of 0.126. In addition, the proposed model could outperform the state‐of‐the‐art CNN in identifying brain hemorrhages.

show abstract

Intelligent brain hemorrhage diagnosis using artificial neural networks

Cited by 23 publications

References 7 publications

Application of Deep Learning in Neuroradiology: Brain Haemorrhage Classification Using Transfer Learning

Application of Deep Learning in Neuroradiology: Brain Haemorrhage Classification Using Transfer Learning

Intracranial Hemorrhage Detection in Ct Scan Using Deep Learning

An efficient stacked bidirectional GRU‐LSTM network for intracranial hemorrhage detection

Contact Info

Product

Resources

About