Classification of stroke disease using convolutional neural network

Marbun, Jodiaman Tua; Seniman,; Andayani, U

doi:10.1088/1742-6596/978/1/012092

Cited by 31 publications

(15 citation statements)

References 2 publications

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“…The data preparation process involves collecting data and data augmentation using the same data as [2]. The data has three classes: Normal, Hemorrhagic Stroke, and Ischemic Stroke; each class has 10 data (Fig.…”

Section: Data Preparationmentioning

confidence: 99%

“…It is a condition that arises due to circulatory disorders in the brain, causes a person suffering from paralysis or death [1]. Stroke recognition is difficult because people do not regularly check up their brain and heart conditions [2]. The general diagnosis procedure uses Computed Tomography (CT) scans, Magnetic Resonance Imaging (MRI) and Electrocardiogram (EKG or ECG) [3].…”

Section: Introductionmentioning

confidence: 99%

“…They obtain the highest accuracy of 88.55% using the MLP approach. Likewise, Marbun et al [2] used CT brain scan images to detect strokes with preprocessing consisting of gray scaling, scaling, cloning, and then segmentation, namely binary image formation using thresholding. For classification, they used the Convolutional Neural Network method and obtained an accuracy value of 90%.…”

Section: Introductionmentioning

confidence: 99%

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Improving stroke diagnosis accuracy using hyperparameter optimized deep learning

Badriyah

Santoso

Syarif

et al. 2019

Int. J. Adv. Intell. Informatics

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Stroke may cause death for anyone, including youngsters. One of the early stroke detection techniques is a Computerized Tomography (CT) scan. This research aimed to optimize hyperparameter in Deep Learning, Random Search and Bayesian Optimization for determining the right hyperparameter. The CT scan images were processed by scaling, grayscale, smoothing, thresholding, and morphological operation. Then, the images feature was extracted by the Gray Level Co-occurrence Matrix (GLCM). This research was performed a feature selection to select relevant features for reducing computing expenses, while deep learning based on hyperparameter setting was used to the data classification process. The experiment results showed that the Random Search had the best accuracy, while Bayesian Optimization excelled in optimization time.

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Section: Data Preparationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improving stroke diagnosis accuracy using hyperparameter optimized deep learning

Badriyah

Santoso

Syarif

et al. 2019

Int. J. Adv. Intell. Informatics

View full text Add to dashboard Cite

show abstract

“…The health staff identified stroke based on the caused of the brain stroke injury. Based on causes, stroke is classified into two, namely ischemic (in which the blood supply stops flowing to the brain due to blockage) and hemorrhagic (where there is bleeding in the brain tissue) [1]. Based on the classification, the health staff is essential to carry out an appropriate diagnosis before starting stroke treatment due to different disease conditions.…”

Section: Introductionmentioning

confidence: 99%

Quad Convolutional Layers (QCL) CNN Approach for Classification of Brain Stroke in Diffusion Weighted (DW) - Magnetic Resonance Images (MRI)

2022

IJIES

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Commonly, clinicans have problems for recognising brain stroke injury images. However, with the advantages of Information technology it is expected that will be a new method that can support the clinicans' opinion for recognising the brain stroke injury for type of stroke (hemorrhagic, ischemic, and normal). Therefore, this study aim is to discovery a new model to classify hemorrhagic, ischemic and normal based on Diffusion Weighted (DW)-Magnetic Resonance (MR) images. This study argues by using Qual Convolutional Layers (QCL-CNN) which applied in CNN can classified type of stroke. For this study experiment, this research conducted two experiment to asses the performance of QCL-CNN. The first experiments partitioned the MR image dataset into 20 percent testing and 80 percent training sets. Then, the second testing performed ten-fold cross-validation on the image dataset. The result from the first experiment of the classification accuracies obtained 93.90 percent (1 st dataset) and 94.96 percent (2 nd dataset). As for the second experiment, the results shows that the classification accuracies obtained 95.91 percent ( 1 st data set) and 97.31 percent ( 2 nd data set). The data source for this study gained from Indonesian hospital and the web sources dataset public from Ischemic Stroke Lesion Segmentation (ISLES). This study also compared, the QCL-CNN model with other architecture model such as AlexNet, ResNet50, and VGG16. The result of the comparison experiment shows that QCL-CNN architectures model has excellent performance than the others model.

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“…Deep learning method that mainly used for image problem is convolutional neural network (CNN). Several researchers have used CNN to classify medical imaging results such as [13] for classifying stroke, [14] for classifying type of muscle, and [15] for classifying abdominal ultrasound images. The used of CNN for classifying CXR has also been performed in [16] and [17] for classifying two classes, normal condition and pneumonia.…”

mentioning

confidence: 99%

Classification of pneumonia from X-ray images using siamese convolutional network

2020

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Pneumonia is one of the highest global causes of deaths especially for children under 5 years old. This happened mainly because of the difficulties in identifying the cause of pneumonia. As a result, the treatment given may not be suitable for each pneumonia case. Recent studies have used deep learning approaches to obtain better classification within the cause of pneumonia. In this research, we used siamese convolutional network (SCN) to classify chest x-ray pneumonia image into 3 classes, namely normal conditions, bacterial pneumonia, and viral pneumonia. Siamese convolutional network is a neural network architecture that learns similarity knowledge between pairs of image inputs based on the differences between its features. One of the important benefits of classifying data with SCN is the availability of comparable images that can be used as a reference when determining class. Using SCN, our best model achieved 80.03% accuracy, 79.59% f1 score, and an improved result reasoning by providing the comparable images.

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Classification of stroke disease using convolutional neural network

Cited by 31 publications

References 2 publications

Improving stroke diagnosis accuracy using hyperparameter optimized deep learning

Improving stroke diagnosis accuracy using hyperparameter optimized deep learning

Quad Convolutional Layers (QCL) CNN Approach for Classification of Brain Stroke in Diffusion Weighted (DW) - Magnetic Resonance Images (MRI)

Classification of pneumonia from X-ray images using siamese convolutional network

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