An automated early ischemic stroke detection system using CNN deep learning algorithm

Chin, Chiun-Li; Lin, Bing-Jhang; Wu, Guei-Ru; Weng, Tzu-Chieh; Yang, Cheng-Shiun; Su, Rui-Cih; Pan, Yu-Jen

doi:10.1109/icawst.2017.8256481

Cited by 91 publications

(43 citation statements)

References 6 publications

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“…They perform their proposed method on 300 CT brain images and have achieved 99% accuracy. Chin et al [20] develop a CNN model from scratch for the patches classification purpose. They have reported accuracy performance with more than 90% for 256 CT images.…”

Section: Comparison To Other Methodsmentioning

confidence: 99%

“…The emergence of DL in medical image classification with diseases such as Alzheimer, brain tumour ischemic infarction and dementia are tremendously increasing with high performance [18]. However, only very recently have other researchers started to investigate the possibility of ischemic image classification with acute condition based on the DL method [12] [19] [20]. To the best of author knowledge, this is the first study to explore the challenge of classifying normal and ischemic primarily in PF slices using DL.…”

Section: Introductionmentioning

confidence: 99%

“…They use CIFAR-10 and ImageNet architecture as a deep convolutional network model for classification. Chin et al [20] develop a CNN network to classify ischemic using image patches. They use two convolutional layers, one max pooling layer and a single fully connected layer.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Deep Transfer Learning Application for Automated Ischemic Classification in Posterior Fossa CT Images

Suberi¹,

Nurshazwani²,

Tomari³

et al. 2019

IJACSA

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Computed Tomography (CT) imaging is one of the conventional tools used to diagnose ischemic in Posterior Fossa (PF). Radiologist commonly diagnoses ischemic in PF through CT imaging manually. However, such a procedure could be strenuous and time consuming for large scale images, depending on the expertise and ischemic visibility. With the rapid development of computer technology, automatic image classification based on Machine Learning (ML) is widely been developed as a second opinion to the ischemic diagnosis. The practical performance of ML is challenged by the emergence of deep learning applications in healthcare. In this study, we evaluate the performance of deep transfer learning models of Convolutional Neural Network (CNN); VGG-16, GoogleNet and ResNet-50 to classify the normal and abnormal (ischemic) brain CT images of PF. This is the first study that intensively studies the application of deep transfer learning for automated ischemic classification in the posterior part of brain CT images. The experimental results show that ResNet-50 is capable to achieve the highest accuracy performance in comparison to other proposed models. Overall, this automatic classification provides a convenient and time-saving tool for improving medical diagnosis.

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Section: Comparison To Other Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Deep Transfer Learning Application for Automated Ischemic Classification in Posterior Fossa CT Images

Suberi¹,

Nurshazwani²,

Tomari³

et al. 2019

IJACSA

View full text Add to dashboard Cite

show abstract

“…Currently, many deep learning-based studies use CT or MRI images to detect stroke [ 26 , 27 , 28 , 29 , 30 , 31 , 32 ]. For example, in a study classifying hemorrhagic stroke and ischemic stroke using brain CT images, Gautam et al [ 26 ] achieved a classifier performance of up to 98.77%.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning-Based Stroke Disease Prediction System Using Real-Time Bio Signals

Choi

Park

Jun

et al. 2021

Sensors

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The emergence of an aging society is inevitable due to the continued increases in life expectancy and decreases in birth rate. These social changes require new smart healthcare services for use in daily life, and covid-19 has also led to a contactless trend necessitating more non-face-to-face health services. Due to the improvements that have been achieved in healthcare technologies, an increasing number of studies have attempted to predict and analyze certain diseases in advance. Research on stroke diseases is actively underway, particularly with the aging population. Stroke, which is fatal to the elderly, is a disease that requires continuous medical observation and monitoring, as its recurrence rate and mortality rate are very high. Most studies examining stroke disease to date have used MRI or CT images for simple classification. This clinical approach (imaging) is expensive and time-consuming while requiring bulky equipment. Recently, there has been increasing interest in using non-invasive measurable EEGs to compensate for these shortcomings. However, the prediction algorithms and processing procedures are both time-consuming because the raw data needs to be separated before the specific attributes can be obtained. Therefore, in this paper, we propose a new methodology that allows for the immediate application of deep learning models on raw EEG data without using the frequency properties of EEG. This proposed deep learning-based stroke disease prediction model was developed and trained with data collected from real-time EEG sensors. We implemented and compared different deep-learning models (LSTM, Bidirectional LSTM, CNN-LSTM, and CNN-Bidirectional LSTM) that are specialized in time series data classification and prediction. The experimental results confirmed that the raw EEG data, when wielded by the CNN-bidirectional LSTM model, can predict stroke with 94.0% accuracy with low FPR (6.0%) and FNR (5.7%), thus showing high confidence in our system. These experimental results demonstrate the feasibility of non-invasive methods that can easily measure brain waves alone to predict and monitor stroke diseases in real time during daily life. These findings are expected to lead to significant improvements for early stroke detection with reduced cost and discomfort compared to other measuring techniques.

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“…In recent years, deep learning has been widely applied in the biomedical field to perform tasks such as medical image segmentation [1] and drug binding prediction [2]. Particularly in the neuroimaging domain, research efforts focus on applying deep learning to perform clinical tasks such as imagebased stroke detection [3], Magenetic Resonance-Computed Tomography (MR-CT) modality transfer [4] and detection of neurodegenerative diseases [5].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Multi-Channel Eeg Classification with Gramian Temporal Generative Adversarial Networks

Kan

Povinelli

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Deep learning's requirements for large amounts of training data remains a challenge for researchers and developers. Generative Adversarial Network (GAN) is commonly used in medical image analysis to generate novel training images to help resolve this issue. While deep learning has many clinical applications in radiology, its applications in medical time series data such as electroencephalogram (EEG) are usually constrained to 1 dimension. Hence, there are few available GAN architectures that effectively synthesize single and multi-channel EEG. In this paper, we propose a novel method to synthesize multi-channel EEG in the form of Gramian Angular Field (GAF) images with a Gramian Temporal Generative Adversarial Network (GT-GAN). The proposed network is capable of generating realistic GAF images and enhances EEG anomaly detection accuracy in residual learning frameworks.

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An automated early ischemic stroke detection system using CNN deep learning algorithm

Cited by 91 publications

References 6 publications

Deep Transfer Learning Application for Automated Ischemic Classification in Posterior Fossa CT Images

Deep Transfer Learning Application for Automated Ischemic Classification in Posterior Fossa CT Images

Deep Learning-Based Stroke Disease Prediction System Using Real-Time Bio Signals

Enhancing Multi-Channel Eeg Classification with Gramian Temporal Generative Adversarial Networks

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