A Deep-Learning Model with Learnable Group Convolution and Deep Supervision for Brain Tumor Segmentation

Liu, Hengxin; Li, Qiang; Wang, I-Chi

doi:10.1155/2021/6661083

Cited by 4 publications

(3 citation statements)

References 43 publications

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“…A ResNet method can be utilized to address this problem. A gain of the relationship between these factors residual layer in ResNet is combined with all of its direct input to become its next inner layer [ 27 – 29 ]. Let H(RX) denote a residual mapping to establish a deep residual block, as shown in Fig.…”

Section: Related Workmentioning

confidence: 99%

An early detection and segmentation of Brain Tumor using Deep Neural Network

Aggarwal

Tiwari²,

Mangipudi

et al. 2023

BMC Med Inform Decis Mak

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Background Magnetic resonance image (MRI) brain tumor segmentation is crucial and important in the medical field, which can help in diagnosis and prognosis, overall growth predictions, Tumor density measures, and care plans needed for patients. The difficulty in segmenting brain Tumors is primarily because of the wide range of structures, shapes, frequency, position, and visual appeal of Tumors, like intensity, contrast, and visual variation. With recent advancements in Deep Neural Networks (DNN) for image classification tasks, intelligent medical image segmentation is an exciting direction for Brain Tumor research. DNN requires a lot of time & processing capabilities to train because of only some gradient diffusion difficulty and its complication. Methods To overcome the gradient issue of DNN, this research work provides an efficient method for brain Tumor segmentation based on the Improved Residual Network (ResNet). Existing ResNet can be improved by maintaining the details of all the available connection links or by improving projection shortcuts. These details are fed to later phases, due to which improved ResNet achieves higher precision and can speed up the learning process. Results The proposed improved Resnet address all three main components of existing ResNet: the flow of information through the network layers, the residual building block, and the projection shortcut. This approach minimizes computational costs and speeds up the process. Conclusion An experimental analysis of the BRATS 2020 MRI sample data reveals that the proposed methodology achieves competitive performance over the traditional methods like CNN and Fully Convolution Neural Network (FCN) in more than 10% improved accuracy, recall, and f-measure.

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Section: Related Workmentioning

confidence: 99%

An early detection and segmentation of Brain Tumor using Deep Neural Network

Aggarwal

Tiwari²,

Mangipudi

et al. 2023

BMC Med Inform Decis Mak

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“…This approach is mainly used to extract the features that belong to tumor parts in MRI. Liu et al, [24] discussed the DL approach that learns the set of convolution and deep supervision. This technique is replaced with the feature extraction that leans with group convolution.…”

Section: Role Of Deep Learning (Dl)mentioning

confidence: 99%

Brain Tumor Detection using Integrated Learning Process Detection (ILPD)

Praveena¹,

Rao²

2022

IJACSA

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Brain tumor detection becomes more complicated process in medical image processing. Analyzing brain tumors is very difficult task because of the unstructured shape of the tumors. Generally, tumors are of two types such as cancerous and non-cancerous. Cancerous tumors are called malignant and non-cancerous are called benign tumors. Malignant tumors are more complex to the patients if these are not detected in the early stages. Precancerous are the other types of tumors that may become cancerous if the treatment is not taken in the early stages. Machine Learning (ML) approaches are most widely used to detect complex patterns but ML has various disadvantages such as time taking process to detect brain tumors. In this paper, integrated learning process detection (ILPD) is introduced to detect the tumors in the brain and analyzes the shape and size of the tumors, and find the stage of the tumors in the given input image. To increase the tumor detection rate advanced image filters are adopted with Deep Convolutional Neural Networks (D-CNN) to improve the detection rate. A pre-trained model called VGG19 is applied to train the MRI brain images for effective detection of tumors. Two benchmark datasets are collected from Kaggle and BraTS 2019 contains MRI brain scan images. The performance of the proposed approach is analyzed by showing the accuracy, f1-score, sensitivity, dice similarity score and specificity.

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“…A ResNet-based deep learning technique termed a learnable group convolution network for brain segmentation has been established by Liu et al [19]. The proposed work compacts with minimizing the layer of convolution also with better segmentation.…”

Section: Introductionmentioning

confidence: 99%

Efficient Morphological Segmentation of Brain Hemorrhage Stroke Lesion Through MultiResUNet

Shijitha¹,

Karthigaikumar²,

Paul³

2022

Computers, Materials &Amp; Continua

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Brain Hemorrhagic stroke is a serious malady that is caused by the drop in blood flow through the brain and causes the brain to malfunction. Precise segmentation of brain hemorrhage is crucial, so an enhanced segmentation is carried out in this research work. The brain image of various patients has taken using an MRI scanner by the utilization of T1, T2, and FLAIR sequence. This work aims to segment the Brain Hemorrhagic stroke using deep learning-based Multi-resolution UNet (multires UNet) through morphological operations. It is hard to precisely segment the brain lesions to extract the existing region of stroke. This crucial step is accomplished by this proposed MMU-Net methodology by precise segmentation of stroke lesions. The proposed method efficiently determines the hemorrhagic stroke with improved accuracy of 95% compared with the existing segmentation techniques such as U-net++, ResNet, Multires UNET and 3D-ResU-Net and also provides improved performance of 2D and 3D U-Net with an enhanced outcome. The performance measure of the proposed methodology acquires an improved accuracy, precision ratio, sensitivity, and specificity rate of 0.07%, 0.04%, 0.04%, and 0.05% in comparison to U-net, ResNet, Multires UNET and 3D-ResU-Net techniques respectively.

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A Deep-Learning Model with Learnable Group Convolution and Deep Supervision for Brain Tumor Segmentation

Cited by 4 publications

References 43 publications

An early detection and segmentation of Brain Tumor using Deep Neural Network

An early detection and segmentation of Brain Tumor using Deep Neural Network

Brain Tumor Detection using Integrated Learning Process Detection (ILPD)

Efficient Morphological Segmentation of Brain Hemorrhage Stroke Lesion Through MultiResUNet

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