Seg-Net: Automatic Lung Infection Segmentation of COVID-19 from CT images

Rajakumar, G.; Leela, R. Sharon Jeba; Darney, P. Ebby; Narayanan, K. Lakshmi; Krishnan, R. Santhana; Robinson, Y. Harold

doi:10.1109/icoei51242.2021.9453022

Cited by 4 publications

(2 citation statements)

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“…A ResNet consists of many layers; each layer has its own number of layers, such as 34, 50, 101, 152, and even 1202. ResNet50 is a well-known network with 49 convolutional layers and one FC [23]. ▪ VGG16 was proposed by [24] as a CNN model.…”

Section: A Deep Learning (Dl)mentioning

confidence: 99%

Deep Learning Approach for Advanced COVID-19 Analysis

Alhalaseh,

Abbadi,

Kassasbeh

2023

IJITEE

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Since the spread of the COVID-19 pandemic, the number of patients has increased dramatically, making it difficult for medical staff, including doctors, to cover hospitals and monitor patients. Therefore, this work depends on Computerized Tomography (CT) scan images to diagnose COVID-19. CT scan images are used to diagnose and determine the severity of the disease. On the other hand, Deep Learning (DL) is widely used in medical research, making great progress in medical technologies. For the diagnosis process, the Convolutional Neural Network (CNN) algorithm is used as a type of DL algorithm. Hence, this work focuses on detecting COVID-19 from CT scan images and determining the severity of the illness. The proposed model is as follows: first, classifying CT scan images into infected or not infected using one of the CNN structures, Residual Neural Networks (ResNet50); second, applying a segmentation process for the infected images to identify lungs and pneumonia using the SegNet algorithm (a CNN architecture for semantic pixel-wise segmentation) so that the disease's severity can be determined; finally, applying linear regression to predict the disease's severity for any new image. The proposed approach reached an accuracy of 95.7% in the classification process and lung and pneumonia segmentation of 98.6% and 96.2%, respectively. Furthermore, a regression process reached an accuracy of 98.29%.

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Section: A Deep Learning (Dl)mentioning

confidence: 99%

Deep Learning Approach for Advanced COVID-19 Analysis

Alhalaseh,

Abbadi,

Kassasbeh

2023

IJITEE

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“…In the field of medical image processing, several outstanding convolutional neural networks (CNNs) have been implemented, and as a result, the most advanced performance possible has been attained [7,11,15]. Researchers used the U-net model and its application to the lung segmentation process utilizing X-rays.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of Chest X-Ray Images Using U-Net Model

Kamil

Hashem

2022

mendel

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Medical imaging, such as chest X-rays, gives an acceptable image of lung functions. Manipulating these images by a radiologist is difficult, thus delaying the diagnosis. Coronavirus is a disease that affects the lung area. Lung segmentation has a significant function in assessing lung disorders. The process of segmentation has seen widespread use of deep learning algorithms. The U-Net is one of the most significant semantic segmentation frameworks for a convolutional neural network. In this paper, the proposed U-Net architecture is evaluated on 565 datasets divided into 500 training images and 65 validation images, For chest X-ray. The findings of the experiments demonstrate that the suggested strategy successfully achieved competitive outcomes with 91.47% and 89.18% accuracy, 0.7494 and 0.7480 IoU, 19.23% and 26.11% loss for training and validation images, respectively.

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