Query: lung image covid

title: Automatic X ray COVID 19 Lung Image Classification System based on Multi Level Thresholding and Support Vector Machine
abstract: The early detection of SARS CoV 2, the causative agent of (COVID 19) is now a critical task for the clinical practitioners. The COVID 19 spread is announced as pandemic outbreak between people worldwide by WHO since 11/ March/ 2020. In this consequence, it is top critical priority to become aware of the infected people so that prevention procedures can be processed to minimize the COVID 19 spread and to begin early medical health care of those infected persons. In this paper, the deep studying based totally methodology is usually recommended for the detection of COVID 19 infected patients using X ray images. The help vector gadget classifies the corona affected X ray images from others through usage of the deep features. The technique is useful for the clinical practitioners for early detection of COVID 19 infected patients. The suggested system of multi level thresholding plus SVM presented high accuracy in classification of the infected lung with Covid 19. All images were of the same size and stored in JPEG format with 512 512 pixels. The average sensitivity, specificity, and accuracy of the lung classification using the proposed model results were 95.76% 99.7% and 97.48% respectively.

title: Diagnosis and detection of infected tissue of COVID 19 patients based on lung x ray image using convolutional neural network approaches
abstract: COVID 19 pandemic has challenged the world science. The international community tries to find, apply, or design novel methods for diagnosis and treatment of COVID 19 patients as soon as possible. Currently, a reliable method for the diagnosis of infected patients is a reverse transcription polymerase chain reaction. The method is expensive and time consuming. Therefore, designing novel methods is important. In this paper, we used three deep learning based methods for the detection and diagnosis of COVID 19 patients with the use of X Ray images of lungs. For the diagnosis of the disease, we presented two algorithms include deep neural network (DNN) on the fractal feature of images and convolutional neural network (CNN) methods with the use of the lung images, directly. Results classification shows that the presented CNN architecture with higher accuracy (93.2% and sensitivity (96.1% is outperforming than the DNN method with an accuracy of 83.4% and sensitivity of 86% In the segmentation process, we presented a CNN architecture to find infected tissue in lung images. Results show that the presented method can almost detect infected regions with high accuracy of 83.84% This finding also can be used to monitor and control patients from infected region growth.

title: Automated Chest CT Image Segmentation of COVID 19 Lung Infection based on 3D U Net
abstract: The coronavirus disease 2019 (COVID 19) affects billions of lives around the world and has a significant impact on public healthcare. Due to rising skepticism towards the sensitivity of RT PCR as screening method, medical imaging like computed tomography offers great potential as alternative. For this reason, automated image segmentation is highly desired as clinical decision support for quantitative assessment and disease monitoring. However, publicly available COVID 19 imaging data is limited which leads to overfitting of traditional approaches. To address this problem, we propose an innovative automated segmentation pipeline for COVID 19 infected regions, which is able to handle small datasets by utilization as variant databases. Our method focuses on on the fly generation of unique and random image patches for training by performing several preprocessing methods and exploiting extensive data augmentation. For further reduction of the overfitting risk, we implemented a standard 3D U Net architecture instead of new or computational complex neural network architectures. Through a 5 fold cross validation on 20 CT scans of COVID 19 patients, we were able to develop a highly accurate as well as robust segmentation model for lungs and COVID 19 infected regions without overfitting on the limited data. Our method achieved Dice similarity coefficients of 0.956 for lungs and 0.761 for infection. We demonstrated that the proposed method outperforms related approaches, advances the state of the art for COVID 19 segmentation and improves medical image analysis with limited data. The code and model are available under the following link: this https URL

title: A Quantitative Lung Computed Tomography Image Feature for Multi Center Severity Assessment of COVID 19
abstract: The COVID 19 pandemic has affected millions and congested healthcare systems globally. Hence an objective severity assessment is crucial in making therapeutic decisions judiciously. Computed Tomography (CT) scans can provide demarcating features to identify the severity of pneumonia, commonly associated with COVID 19, in the affected lungs. Here, a semi quantitative severity assessing lung CT image feature is demonstrated for COVID 19 patients. An open source multi center Italian database was used, among which 61 cases were incorporated in the study (age 27 86, 71% males) from 27 CT imaging centers. Lesions in the form of opacifications, crazy paving patterns, and consolidations were heuristically marked and the severity determining feature L_norm was quantified and established to be statistically distinct for the three classes i.e. mild, moderate, and severe (p value 0.0001) The thresholds of L_norm for a 3 class classification were determined based on the optimum sensitivity/specificity combination from ROC analyses. The feature L_norm classified the cases in the three severity categories with 89.34% accuracy when L_norm was calculated based on experts' heuristic annotations. Substantial to almost perfect intra rater and inter rater agreements were achieved involving expert and non expert based evaluations {kappa} score 0.79 0.97) We trained machine learning based classification models and showed L_norm alone has a superior diagnostic accuracy over standard image intensity and texture features. Classification accuracy was further increased when L_norm was used to delineate the severe cases from non severe ones (98.9% with high sensitivity (97.7% and specificity (97.49% Therefore, key highlights of this severity assessment feature are accuracy, lower dependency on expert availability, and wide utility across multiple imaging centers.

title: Deep learning to estimate the physical proportion of infected region of lung for COVID 19 pneumonia with CT image set
abstract: Utilizing computed tomography (CT) images to quickly estimate the severity of cases with COVID 19 is one of the most straightforward and efficacious methods. Two tasks were studied in this present paper. One was to segment the mask of intact lung in case of pneumonia. Another was to generate the masks of regions infected by COVID 19. The masks of these two parts of images then were converted to corresponding volumes to calculate the physical proportion of infected region of lung. A total of 129 CT image set were herein collected and studied. The intrinsic Hounsfiled value of CT images was firstly utilized to generate the initial dirty version of labeled masks both for intact lung and infected regions. Then, the samples were carefully adjusted and improved by two professional radiologists to generate the final training set and test benchmark. Two deep learning models were evaluated: UNet and 2.5D UNet. For the segment of infected regions, a deep learning based classifier was followed to remove unrelated blur edged regions that were wrongly segmented out such as air tube and blood vessel tissue etc. For the segmented masks of intact lung and infected regions, the best method could achieve 0.972 and 0.757 measure in mean Dice similarity coefficient on our test benchmark. As the overall proportion of infected region of lung, the final result showed 0.961 (Pearson's correlation coefficient) and 11.7% (mean absolute percent error) The instant proportion of infected regions of lung could be used as a visual evidence to assist clinical physician to determine the severity of the case. Furthermore, a quantified report of infected regions can help predict the prognosis for COVID 19 cases which were scanned periodically within the treatment cycle.

title: Lung CT image aided detection COVID 19 based on Alexnet network
abstract: In this article, we use the Alexnet network in deep learning to determine whether the lung CT images are infected with covid 19.First of all, in the data preprocessing stage, the original CT image data is scaled and normalized to reduce noise interference. The batch operation of training set and test set can increase the training speed;Secondly, build an eight layer Alexnet network model, set reasonable hyperparameters for each layer of the network, define the loss function and optimizer, and use the processed data to train the weight parameters of each layer in the network model.Finally, three indicators of accuracy, accuracy and recall are used to quantify the effect of model classification, and the impact of different training times on these three indicators is compared to select the best classification model.At the same time, use pyqt5 to write the corresponding auxiliary detection interface to facilitate the display of test results and the selection of classification models.The construction of the network model, the definition of the loss function and the definition of the optimizer are all based on the Pytorch deep learning framework.

title: CT image visual quantitative evaluation and clinical classification of coronavirus disease (COVID 19)
abstract: To explore the relationship between the imaging manifestations and clinical classification of COVID 19.  We conducted a retrospective single center study on patients with COVID 19 from Jan. 18, 2020  2020 in Zhuhai, China. Patients were divided into 3 types based on Chinese guideline: mild (patients with minimal symptoms and negative CT findings) common, and severe critical (patients with positive CT findings and different extent of clinical manifestations) CT visual quantitative evaluation was based on summing up the acute lung inflammatory lesions involving each lobe, which was scored as 0 (0% 1 (1 25% 2 (26 50% 3 (51 75% or 4 (76 100% respectively. The total severity score (TSS) was reached by summing the five lobe scores. The consistency of two observers was evaluated. The TSS was compared with the clinical classification. ROC was used to test the diagnosis ability of TSS for severe critical type.  This study included 78 patients, 38 males and 40 females. There were 24 mild (30.8% 46 common (59.0% and 8 severe critical (10.2% cases, respectively. The median TSS of severe critical type group was significantly higher than common type p 0.001) The ICC value of the two observers was 0.976 (95% CI 0.962 0.985) ROC analysis showed the area under the curve (AUC) of TSS for diagnosing severe critical type was 0.918. The TSS cutoff of 7.5 had 82.6% sensitivity and 100% specificity.  The proportion of clinical mild type patients with COVID 19 was relatively high; CT was not suitable for independent screening tool. The CT visual quantitative analysis has high consistency and can reflect the clinical classification of COVID 19.  CT visual quantitative evaluation has high consistency (ICC value of 0.976) among the observers. The median TSS of severe critical type group was significantly higher than common type p 0.001) ROC analysis showed the area under the curve (AUC) of TSS for diagnosing severe critical type was 0.918 (95% CI 0.843 0.994) The TSS cutoff of 7.5 had 82.6% sensitivity and 100% specificity. The proportion of confirmed COVID 19 patients with normal chest CT was relatively high (30.8% CT was not a suitable screening modality

title: COVID 19 Chest CT Image Segmentation A Deep Convolutional Neural Network Solution
abstract: A novel coronavirus disease 2019 (COVID 19) was detected and has spread rapidly across various countries around the world since the end of the year 2019, Computed Tomography (CT) images have been used as a crucial alternative to the time consuming RT PCR test. However, pure manual segmentation of CT images faces a serious challenge with the increase of suspected cases, resulting in urgent requirements for accurate and automatic segmentation of COVID 19 infections. Unfortunately, since the imaging characteristics of the COVID 19 infection are diverse and similar to the backgrounds, existing medical image segmentation methods cannot achieve satisfactory performance. In this work, we try to establish a new deep convolutional neural network tailored for segmenting the chest CT images with COVID 19 infections. We firstly maintain a large and new chest CT image dataset consisting of 165,667 annotated chest CT images from 861 patients with confirmed COVID 19. Inspired by the observation that the boundary of the infected lung can be enhanced by adjusting the global intensity, in the proposed deep CNN, we introduce a feature variation block which adaptively adjusts the global properties of the features for segmenting COVID 19 infection. The proposed FV block can enhance the capability of feature representation effectively and adaptively for diverse cases. We fuse features at different scales by proposing Progressive Atrous Spatial Pyramid Pooling to handle the sophisticated infection areas with diverse appearance and shapes. We conducted experiments on the data collected in China and Germany and show that the proposed deep CNN can produce impressive performance effectively.

title: CovidCTNet: An Open Source Deep Learning Approach to Identify Covid 19 Using CT Image
abstract: Coronavirus disease 2019 (Covid 19) is highly contagious with limited treatment options. Early and accurate diagnosis of Covid 19 is crucial in reducing the spread of the disease and its accompanied mortality. Currently, detection by reverse transcriptase polymerase chain reaction (RT PCR) is the gold standard of outpatient and inpatient detection of Covid 19. RT PCR is a rapid method, however, its accuracy in detection is only ~70 75% Another approved strategy is computed tomography (CT) imaging. CT imaging has a much higher sensitivity of ~80 98% but similar accuracy of 70% To enhance the accuracy of CT imaging detection, we developed an open source set of algorithms called CovidCTNet that successfully differentiates Covid 19 from community acquired pneumonia (CAP) and other lung diseases. CovidCTNet increases the accuracy of CT imaging detection to 90% compared to radiologists (70% The model is designed to work with heterogeneous and small sample sizes independent of the CT imaging hardware. In order to facilitate the detection of Covid 19 globally and assist radiologists and physicians in the screening process, we are releasing all algorithms and parametric details in an open source format. Open source sharing of our CovidCTNet enables developers to rapidly improve and optimize services, while preserving user privacy and data ownership.

title: Harmony Search and Otsu based System for Coronavirus Disease (COVID 19) Detection using Lung CT Scan Images
abstract: Pneumonia is one of the foremost lung diseases and untreated pneumonia will lead to serious threats for all age groups. The proposed work aims to extract and evaluate the Coronavirus disease (COVID 19) caused pneumonia infection in lung using CT scans. We propose an image assisted system to extract COVID 19 infected sections from lung CT scans (coronal view) It includes following steps: (i) Threshold filter to extract the lung region by eliminating possible artifacts; (ii) Image enhancement using Harmony Search Optimization and Otsu thresholding; (iii) Image segmentation to extract infected region(s) and (iv) Region of interest (ROI) extraction (features) from binary image to compute level of severity. The features that are extracted from ROI are then employed to identify the pixel ratio between the lung and infection sections to identify infection level of severity. The primary objective of the tool is to assist the pulmonologist not only to detect but also to help plan treatment process. As a consequence, for mass screening processing, it will help prevent diagnostic burden.