Convolutional-Neural-Network Assisted Segmentation and SVM Classification of Brain Tumor in Clinical MRI Slices

Rajinikanth, V.; Kadry, Seifedine; Nam, Yunyoung

doi:10.5755/j01.itc.50.2.28087

Cited by 53 publications

(42 citation statements)

References 35 publications

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“…The architecture of the VGG-UNet and VGG-SegNet is depicted in Figures 5 and 6 , respectively. The necessary information on VGG-UNet and VGG-SegNet can be found in [ 28 – 32 ].…”

Section: Methodsmentioning

confidence: 99%

VGG-UNet/VGG-SegNet Supported Automatic Segmentation of Endoplasmic Reticulum Network in Fluorescence Microscopy Images

et al. 2022

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This research work aims to implement an automated segmentation process to extract the endoplasmic reticulum (ER) network in fluorescence microscopy images (FMI) using pretrained convolutional neural network (CNN). The threshold level of the raw FMT is complex, and extraction of the ER network is a challenging task. Hence, an image conversion procedure is initially employed to reduce its complexity. This work employed the pretrained CNN schemes, such as VGG-UNet and VGG-SegNet, to mine the ER network from the chosen FMI test images. The proposed ER segmentation pipeline consists of the following phases; (i) clinical image collection, 16-bit to 8-bit conversion and resizing; (ii) implementation of pretrained VGG-UNet and VGG-SegNet; (iii) extraction of the binary form of ER network; (iv) comparing the mined ER with ground-truth; and (v) computation of image measures and validation. The considered FMI dataset consists of 223 test images, and image augmentation is then implemented to increase these images. The result of this scheme is then confirmed against other CNN methods, such as U-Net, SegNet, and Res-UNet. The experimental outcome confirms a segmentation accuracy of >98% with VGG-UNet and VGG-SegNet. The results of this research authenticate that the proposed pipeline can be considered to examine the clinical-grade FMI.

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“…The architecture of the VGG-UNet and VGG-SegNet is depicted in Figures 5 and 6 , respectively. The necessary information on VGG-UNet and VGG-SegNet can be found in [ 28 – 32 ].…”

Section: Methodsmentioning

confidence: 99%

VGG-UNet/VGG-SegNet Supported Automatic Segmentation of Endoplasmic Reticulum Network in Fluorescence Microscopy Images

et al. 2022

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“…In this work, two different features such as deep and LBP are extracted. The deep features are extracted from the chosen DLM, like GoogleNet, VGG16, VGG19, ResnEt18, ResNEt50 and ResNet101 [14,19,24,27,29,32]. After getting the necessary features, the LBP features with various weights presented in [7] are adopted, and the extracted features are then reduced to avoid the overfitting problem.…”

Section: Feature Extractionmentioning

confidence: 99%

“…The complete information about the VGG16 can be found in [5,24] and in this work, the following parameters are assigned for the VGG16; conventional image augmentation technique.It is assigned during the learning. The learning rate is fixed as 1e-5, Linear-Dropout-Rate (LDR) based training with and Adam optimization.…”

Section: Feature Optimizationmentioning

confidence: 99%

Automatic detection of COVID-19 in chest radiographs using serially concatenated deep and handcrafted features

Kannan

Sivakumar

Ezhilarasi

2022

XST

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Since the infectious disease occurrence rate in the human community is gradually rising due to varied reasons, appropriate diagnosis and treatments are essential to control its spread. The recently discovered COVID-19 is one of the contagious diseases, which infected numerous people globally. This contagious disease is arrested by several diagnoses and handling actions. Medical image-supported diagnosis of COVID-19 infection is an approved clinical practice. This research aims to develop a new Deep Learning Method (DLM) to detect the COVID-19 infection using the chest X-ray. The proposed work implemented two methods namely, detection of COVID-19 infection using (i) a Firefly Algorithm (FA) optimized deep-features and (ii) the combined deep and machine features optimized with FA. In this work, a 5-fold cross-validation method is engaged to train and test detection methods. The performance of this system is analyzed individually resulting in the confirmation that the deep feature-based technique helps to achieve a detection accuracy of > 92% with SVM-RBF classifier and combining deep and machine features achieves > 96% accuracy with Fine KNN classifier. In the future, this technique may have potential to play a vital role in testing and validating the X-ray images collected from patients suffering from the infection diseases.

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“…The brain is one of the prime internal organs in human physiology, and the abnormality in the brain is a major medical emergency. The abnormality in the brain causes various behavioural disorders, and appropriate diagnosis and treatment will help to reduce these issues [ 1 , 2 ]. Multiple sclerosis (MS) is one of the brain-connected diseases that happens mainly in the central nervous system (CNS) [ 3 – 5 ].…”

Section: Introductionmentioning

confidence: 99%

Framework to Segment and Evaluate Multiple Sclerosis Lesion in MRI Slices Using VGG-UNet

Krishnamoorthy

Zhang

Kadry³

et al. 2022

Computational Intelligence and Neuroscience

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Multiple sclerosis (MS) is an autoimmune disease that causes mild to severe issues in the central nervous system (CNS). Early detection and treatment are necessary to reduce the harshness of the disease in individuals. The proposed work aims to implement a convolutional neural network (CNN) segmentation scheme to extract the MS lesion in a 2D brain MRI slice. To achieve a better MS detection, this work implemented the VGG-UNet scheme in which the pretrained VGG19 is considered as the encoder section. This scheme is tested on 30 patient images (600 images with dimension 512 × 512 × 3 pixels), and the experimental outcome confirms that this scheme provides a better result compared to traditional UNet, SegNet, VGG-UNet, and VGG-SegNet. The experimental investigation implemented on axial, coronal and sagittal plane 2D slices of Flair modality confirms that this work provides a better value of Jaccard (>85%), Dice (>92%), and accuracy (>98%).

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Convolutional-Neural-Network Assisted Segmentation and SVM Classification of Brain Tumor in Clinical MRI Slices

Cited by 53 publications

References 35 publications

VGG-UNet/VGG-SegNet Supported Automatic Segmentation of Endoplasmic Reticulum Network in Fluorescence Microscopy Images

VGG-UNet/VGG-SegNet Supported Automatic Segmentation of Endoplasmic Reticulum Network in Fluorescence Microscopy Images

Automatic detection of COVID-19 in chest radiographs using serially concatenated deep and handcrafted features

Framework to Segment and Evaluate Multiple Sclerosis Lesion in MRI Slices Using VGG-UNet

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