Automated Detection of COVID-19 Lesion in Lung CT Slices with VGG-UNet and Handcrafted Features

2021 ◽  
pp. 185-200
Author(s):  
S. Arunmozhi ◽  
Vaddi Satya Sai Sarojini ◽  
T. Pavithra ◽  
Varsha Varghese ◽  
V. Deepti ◽  
...  
Keyword(s):  
Automated Detection ◽  
Lung Ct ◽  
Ct Slices

Automated Detection of Small Pulmonary Nodules in Whole Lung CT Scans

2007 ◽  
Vol 14 (5) ◽  
pp. 579-593 ◽  
Author(s):  
Andinet A. Enquobahrie ◽  
Anthony P. Reeves ◽  
David F. Yankelevitz ◽  
Claudia I. Henschke
Keyword(s):  
Pulmonary Nodules ◽  
Automated Detection ◽  
Ct Scans ◽  
Small Pulmonary Nodules ◽  
Lung Ct

P2P-COVID-GAN

2021 ◽  
Vol 17 (4) ◽  
pp. 101-118
Author(s):  
Nandhini Abirami ◽  
Durai Raj Vincent ◽  
Seifedine Kadry
Keyword(s):  
State Of The Art ◽  
Automatic Segmentation ◽  
Ct Images ◽  
Dice Coefficient ◽  
Computed Tomography Images ◽  
Proposed Model ◽  
Novel Method ◽  
Lung Infections ◽  
Lung Ct ◽  
Ct Slices

Early and automatic segmentation of lung infections from computed tomography images of COVID-19 patients is crucial for timely quarantine and effective treatment. However, automating the segmentation of lung infection from CT slices is challenging due to a lack of contrast between the normal and infected tissues. A CNN and GAN-based framework are presented to classify and then segment the lung infections automatically from COVID-19 lung CT slices. In this work, the authors propose a novel method named P2P-COVID-SEG to automatically classify COVID-19 and normal CT images and then segment COVID-19 lung infections from CT images using GAN. The proposed model outperformed the existing classification models with an accuracy of 98.10%. The segmentation results outperformed existing methods and achieved infection segmentation with accurate boundaries. The Dice coefficient achieved using GAN segmentation is 81.11%. The segmentation results demonstrate that the proposed model outperforms the existing models and achieves state-of-the-art performance.

scholarly journals Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices

2020 ◽  
Vol 51 (1) ◽  
pp. 571-585 ◽  
Author(s):  
Sakshi Ahuja ◽  
Bijaya Ketan Panigrahi ◽  
Nilanjan Dey ◽  
Venkatesan Rajinikanth ◽  
Tapan Kumar Gandhi
Keyword(s):  
Ct Scan ◽  
Transfer Learning ◽  
Automated Detection ◽  
Lung Ct ◽  
Lung Ct Scan

scholarly journals LungINFseg: Segmenting COVID-19 Infected Regions in Lung CT Images Based on a Receptive-Field-Aware Deep Learning Framework

Diagnostics ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 158
Author(s):  
Vivek Kumar Singh ◽  
Mohamed Abdel-Nasser ◽  
Nidhi Pandey ◽  
Domenec Puig
Keyword(s):  
Deep Learning ◽  
Receptive Field ◽  
Contextual Information ◽  
Ct Images ◽  
Learning Ability ◽  
Discrete Wavelet ◽  
Field Frequency ◽  
Segmentation Methods ◽  
Lung Ct ◽  
Ct Slices

COVID-19 is a fast-growing disease all over the world, but facilities in the hospitals are restricted. Due to unavailability of an appropriate vaccine or medicine, early identification of patients suspected to have COVID-19 plays an important role in limiting the extent of disease. Lung computed tomography (CT) imaging is an alternative to the RT-PCR test for diagnosing COVID-19. Manual segmentation of lung CT images is time consuming and has several challenges, such as the high disparities in texture, size, and location of infections. Patchy ground-glass and consolidations, along with pathological changes, limit the accuracy of the existing deep learning-based CT slices segmentation methods. To cope with these issues, in this paper we propose a fully automated and efficient deep learning-based method, called LungINFseg, to segment the COVID-19 infections in lung CT images. Specifically, we propose the receptive-field-aware (RFA) module that can enlarge the receptive field of the segmentation models and increase the learning ability of the model without information loss. RFA includes convolution layers to extract COVID-19 features, dilated convolution consolidated with learnable parallel-group convolution to enlarge the receptive field, frequency domain features obtained by discrete wavelet transform, which also enlarges the receptive field, and an attention mechanism to promote COVID-19-related features. Large receptive fields could help deep learning models to learn contextual information and COVID-19 infection-related features that yield accurate segmentation results. In our experiments, we used a total of 1800+ annotated CT slices to build and test LungINFseg. We also compared LungINFseg with 13 state-of-the-art deep learning-based segmentation methods to demonstrate its effectiveness. LungINFseg achieved a dice score of 80.34% and an intersection-over-union (IoU) score of 68.77%—higher than the ones of the other 13 segmentation methods. Specifically, the dice and IoU scores of LungINFseg were 10% better than those of the popular biomedical segmentation method U-Net.

scholarly journals CoSinGAN: Learning COVID-19 Infection Segmentation from a Single Radiological Image

Diagnostics ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 901
Author(s):  
Pengyi Zhang ◽  
Yunxin Zhong ◽  
Yulin Deng ◽  
Xiaoying Tang ◽  
Xiaoqiong Li
Keyword(s):  
Large Scale ◽  
Early Stage ◽  
Ct Images ◽  
Automated Detection ◽  
Training Data ◽  
Visual Evidence ◽  
Radiological Images ◽  
Radiological Image ◽  
Ct Slices ◽  
Fold Cross Validation

Computed tomography (CT) images are currently being adopted as the visual evidence for COVID-19 diagnosis in clinical practice. Automated detection of COVID-19 infection from CT images based on deep models is important for faster examination. Unfortunately, collecting large-scale training data systematically in the early stage is difficult. To address this problem, we explore the feasibility of learning deep models for lung and COVID-19 infection segmentation from a single radiological image by resorting to synthesizing diverse radiological images. Specifically, we propose a novel conditional generative model, called CoSinGAN, which can be learned from a single radiological image with a given condition, i.e., the annotation mask of the lungs and infected regions. Our CoSinGAN is able to capture the conditional distribution of the single radiological image, and further synthesize high-resolution (512 × 512) and diverse radiological images that match the input conditions precisely. We evaluate the efficacy of CoSinGAN in learning lung and infection segmentation from very few radiological images by performing 5-fold cross validation on COVID-19-CT-Seg dataset (20 CT cases) and an independent testing on the MosMed dataset (50 CT cases). Both 2D U-Net and 3D U-Net, learned from four CT slices by using our CoSinGAN, have achieved notable infection segmentation performance, surpassing the COVID-19-CT-Seg-Benchmark, i.e., the counterparts trained on an average of 704 CT slices, by a large margin. Such results strongly confirm that our method has the potential to learn COVID-19 infection segmentation from few radiological images in the early stage of COVID-19 pandemic.

Automated detection of epithelial changes in colorectal carcinoma

2012 ◽  
Vol 50 (05) ◽  
Author(s):  
G Valcz ◽  
I Bándi ◽  
B Wichmann ◽  
A Patai ◽  
D Szabó ◽  
...  
Keyword(s):  
Colorectal Carcinoma ◽  
Automated Detection

Machine Learning in Agriculture

2018 ◽  
Vol 8 (6) ◽  
pp. 26 ◽  
Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Disease Diagnosis ◽  
Automated Detection ◽  
Disease Prediction ◽  
Paper Briefly ◽  
Agriculture Sector ◽  
Modern Agriculture ◽  
Crop Disease

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data.  Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change.  Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

Sign in / Sign up

Export Citation Format

Share Document