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

doi:10.22266/ijies2022.0228.38

Cited by 2 publications

(2 citation statements)

References 31 publications

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“…To evaluate classification performance, precision, recall, and accuracy matrices are used [16], [17]. To calculate the metric, add the sums of TP, FP, FN, and TN.…”

Section: The Performance Between Machine Learning and Pre-trained Modelsmentioning

confidence: 99%

Deca Convolutional Layer Neural Network (DCL-NN) Method for Categorizing Concrete Cracks in Heritage Building

Nugraheni¹,

Nugroho²,

Dewi³

et al. 2023

IJACSA

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It is critical to develop a method for detecting cracks in historic building concrete structures. This is due to the fact that it is a method of preserving historic building and protecting visitors from the collapse of a historic structure. The purpose of this research is to determine the best method for identifying cracks in the concrete surface of old buildings by using cracked images of old buildings. The various surface textures, crack irregularities, and background complexity that distinguish crack detection from other forms of image detection research present challenges in crack detection of old buildings. This study presents a framework for detecting concrete cracks in old buildings in Semarang's old town using a modified Convolutional Neural Network with a combination of several convolutional layers. This study employs ten convolutional layers (Deca Convolutional Layer Neural Network (DCL-NN)) to provide mapping features for images of concrete cracks in ancient buildings at preservation area. This study also compares commonly used machine learning models such as KNeighbors (n neighbors=3), Random Forest, Support Vector Machine (SVM), ExtraTrees (n estimators=10), and other CNN-pretained models such as VGG19, Xception, and MobileNet. Four performance indicators are used to validate each model's performance: accuracy, recall, precision, F1-score, Matthews Correlation Coefficient (MCC), and Cohen Kappa (CK). This study's data set is comprised of primary data obtained from cracked and normal images of several buildings in Semarang's old town. The accuracy of this study using DCL-NN is 98.87%, recall is 99.40%, precision is 98.33%, F1 is 98.86%, MCC is 97.74%, and CK is 98.86% for crack class. From this study, it was found that the ten convolution layers have higher classification performance compared to other comparison models such as machine learning and other CNN models and are more effective in detecting cracks in concrete structures.

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“…To evaluate classification performance, precision, recall, and accuracy matrices are used [16], [17]. To calculate the metric, add the sums of TP, FP, FN, and TN.…”

Section: The Performance Between Machine Learning and Pre-trained Modelsmentioning

confidence: 99%

Deca Convolutional Layer Neural Network (DCL-NN) Method for Categorizing Concrete Cracks in Heritage Building

Nugraheni¹,

Nugroho²,

Dewi³

et al. 2023

IJACSA

View full text Add to dashboard Cite

show abstract

“…Three CNN models and machine learning algorithms were trained and evaluated by comparing four performance matrices such as: accuracy, precision, recall, and F1-score [16]: Based on the results of the tests that have been carried out, a confusion matrix for Intracerebral Hemorrhage (ICH) and normal brain images can be made using three modified CNN models as presented in Fig. 7.…”

Section: E Performance Matrix For Classificationmentioning

confidence: 99%

A New Method for Classifying Intracerebral Hemorrhage (ICH) Based on Diffusion Weighted –Magnetic Resonance Imaging (DW-MRI)

Nugroho,

Priyanto,

Nugraheni

2023

IJACSA

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Stroke is a condition where the blood supply to the brain is cut off. This occurs due to the rupture of blood vessels in the intracerebral area or Intracerebral Hemorrhage (ICH). Examination by health workers is generally carried out to get an overview of the part of the brain of a patient who has had a stroke. The weakness in diagnosing this disease is that deeper knowledge is needed to classify the type of stroke, especially ICH. This study aims to use the Modified Layers Convolutional Neural Network (ML-CNN) method to classify ICH stroke images based on Diffusion-Weighted (DW) MRI. The data used in this study is a DWI stroke MRI image dataset of 3,484 images. The data consists of 1,742 normal and ICH images validated by a radiologist. Because the data used is relatively small and takes into account the computational time, Stochastic Gradient Descent (SGD) is used. This study compares the basic CNN model scenario with the addition of layers to the original CNN model to produce the highest accuracy value. Furthermore, each model is cross-validated with a different k to produce performance in each model as well as changes to batch size and epoch and comparison with machine learning models such as SVM, Random Forest, Extra Trees, and kNN.The results showed that the smaller the number of batch sizes, the higher the accuracy value and the number of epochs, the higher the number of epochs, the higher the accuracy value of 99.86%. Then, four machine learning methods with accuracy, sensitivity, and specificity below 90% are all compared to CNN2. As a summary of this research, the proposed CNN modification works better than the four machine learning models in classifying stroke images.

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

Cited by 2 publications

References 31 publications

Deca Convolutional Layer Neural Network (DCL-NN) Method for Categorizing Concrete Cracks in Heritage Building

Deca Convolutional Layer Neural Network (DCL-NN) Method for Categorizing Concrete Cracks in Heritage Building

A New Method for Classifying Intracerebral Hemorrhage (ICH) Based on Diffusion Weighted –Magnetic Resonance Imaging (DW-MRI)

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