scholarly journals A Fully Automated Deep Learning-based Network For Detecting COVID-19 from a New And Large Lung CT Scan Dataset

2020 ◽  
Author(s):  
Mohammad Rahimzadeh ◽  
Abolfazl Attar ◽  
Seyed Mohammad Sakhaei
Keyword(s):  
Ct Scan ◽  
Early Stage ◽  
Proper Time ◽  
Ct Images ◽  
Image Sequences ◽  
Image Processing Algorithm ◽  
Automated Method ◽  
The World ◽  
Feature Pyramid ◽  
Lung Ct

AbstractCOVID-19 is a severe global problem that has crippled many industries and killed many people around the world. One of the primary ways to decrease the casualties is the infected person’s identification at the proper time. AI can play a significant role in these cases by monitoring and detecting infected persons in early-stage so that it can help many organizations. In this paper, we aim to propose a fully-automated method to detect COVID-19 from the patient’s CT scan without needing a clinical technician. We introduce a new dataset that contains 48260 CT scan images from 282 normal persons and 15589 images from 95 patients with COVID-19 infection. Our proposed network takes all the CT scan image sequences of a patient as the input and determines if the patient is infected with COVID-19. At the first stage, this network runs an image processing algorithm to discard those CT images that inside the lung is not properly visible in them. This helps to reduce the number of images that shall be identified as normal or COVID-19, so it reduces the processing time. Also, running this algorithm makes the deep network at the next stage to analyze only the proper images and thus reduces false detections. At the next stage, we propose a modified version of ResNet50V2 that is enhanced by a feature pyramid network for classifying the selected CT images into COVID-19 or normal. If enough number of chosen CT scan images of a patient be identified as COVID-19, the network considers that patient, infected to this disease. The ResNet50V2 with feature pyramid network achieved 98.49% accuracy on more than 7996 validation images and correctly identified almost 237 patients from 245 patients.

scholarly journals A Fully Automated Deep Learning-based Network For Detecting COVID-19 from a New And Large Lung CT Scan Dataset

2020 ◽  
Author(s):  
Mohammad Rahimzadeh ◽  
Abolfazl Attar ◽  
Mohammad Sakhaei
Keyword(s):  
Ct Scan ◽  
Early Stage ◽  
Proper Time ◽  
Ct Images ◽  
Image Sequences ◽  
Image Processing Algorithm ◽  
Automated Method ◽  
The World ◽  
Feature Pyramid ◽  
Lung Ct

COVID-19 is a severe global problem that has crippled many industries and killed many people around the world. One of the primary ways to decrease the casualties is the infected person's identification at the proper time. AI can play a significant role in these cases by monitoring and detecting infected persons in early-stage so that it can help many organizations. In this paper, we aim to propose a fully-automated method to detect COVID-19 from the patient's CT scan without needing a clinical technician. We introduce a new dataset that contains 48260 CT scan images from 282 normal persons and 15589 images from 95 patients with COVID-19 infection. Our proposed network takes all the CT scan image sequences of a patient as the input and determines if the patient is infected with COVID-19. At the first stage, this network runs an image processing algorithm to discard those CT images that inside the lung is not properly visible in them. This helps to reduce the number of images that shall be identified as normal or COVID-19, so it reduces the processing time. Also, running this algorithm makes the deep network at the next stage to analyze only the proper images and thus reduces false detections. At the next stage, we propose a modified version of ResNet50V2 that is enhanced by a feature pyramid network for classifying the selected CT images into COVID-19 or normal. If enough number of chosen CT scan images of a patient be identified as COVID-19, the network considers that patient, infected to this disease. The ResNet50V2 with feature pyramid network achieved 98.49% accuracy on more than 7996 validation images and correctly identified almost 237 patients from 245 patients.

scholarly journals A Fully Automated Deep Learning-based Network For Detecting COVID-19 from a New And Large Lung CT Scan Dataset

2020 ◽  
Author(s):  
Mohammad Rahimzadeh ◽  
Abolfazl Attar ◽  
Seyed Mohammad Sakhaei
Keyword(s):  
Ct Scan ◽  
High Speed ◽  
Early Stage ◽  
Proper Time ◽  
Ct Images ◽  
Automated System ◽  
Image Processing Algorithm ◽  
Convolutional Network ◽  
Automated Method ◽  
Feature Pyramid

COVID-19 is a severe global problem, and one of the primary ways to decrease its casualties is the infected person's identification at the proper time. AI can play a significant role in these cases by monitoring and detecting infected persons in early-stage. In this paper, we aim to propose a high- speed and accurate fully-automated method to detect COVID-19 from the patient's CT scan. We introduce a new dataset that contains 48260 CT scan images from 282 normal persons and 15589 images from 95 patients with COVID-19 infection. Our proposed automated system takes all the CT scan image sequences of a patient as the input and determines if the patient is infected with COVID-19. At the first stage, this system runs the proposed image processing algorithm to discard those CT images that inside the lung is not properly visible in them. This helps to reduce the number of images that shall be processed, so it reduces the processing time. Also, running this algorithm makes the deep network at the next stage to analyze only the proper images and thus reduces false detections. At the next stage, we propose a new modified deep convolutional network that is based on ResNet50V2 and is enhanced by the feature pyramid network for classifying the selected CT images into COVID-19 or normal. After running these two phases, if enough number of chosen CT scan images of a patient be identified as COVID-19, the system considers that patient, infected to this disease. In the single image classification stage, the ResNet50V2 with feature pyramid network achieved 98.49% accuracy on more than 7996 validation images. At the fully automated phase, the automated system correctly identified almost 237 patients from 245 patients on average between five-folds with high speed. In the end, we also investigate the classified images with a feature visualization algorithm to indicate the area of infections in each image. We are implementing these materials on some medical centers in Iran, and we hope that it would be a great help in Intelligence disease detection anywhere.

scholarly journals A Fully Automated Deep Learning-based Network For Detecting COVID-19 from a New And Large Lung CT Scan Dataset

2020 ◽  
Author(s):  
Mohammad Rahimzadeh ◽  
Abolfazl Attar ◽  
Seyed Mohammad Sakhaei
Keyword(s):  
Ct Scan ◽  
High Speed ◽  
Early Stage ◽  
Ct Images ◽  
Patient Identification ◽  
Image Processing Algorithm ◽  
Convolutional Networks ◽  
Model Classification ◽  
Automated Method ◽  
Two Phases

COVID-19 is a severe global problem, and AI can play a significant role in preventing losses by monitoring and detecting infected persons in early-stage. This paper aims to propose a high-speed and accurate fully-automated method to detect COVID-19 from the patient's CT scan images. We introduce a new dataset that contains 48260 CT scan images from 282 normal persons and 15589 images from 95 patients with COVID-19 infections. At the first stage, this system runs our proposed image processing algorithm to discard those CT images that inside the lung is not properly visible in them. This action helps to reduce the processing time and false detections. At the next stage, we introduce a novel method for increasing the classification accuracy of convolutional networks. We implemented our method using the ResNet50V2 network and a modified feature pyramid network alongside our designed architecture for classifying the selected CT images into COVID-19 or normal with higher accuracy than other models. After running these two phases, the system determines the condition of the patient using a selected threshold. We are the first to evaluate our system in two different ways. In the single image classification stage, our model achieved 98.49% accuracy on more than 7996 test images. At the patient identification phase, the system correctly identified almost 234 of 245 patients with high speed. We also investigate the classified images with the Grad-CAM algorithm to indicate the area of infections in images and evaluate our model classification correctness.

scholarly journals Reducing False-Positives in Lung Nodules Detection Using Balanced Datasets

2021 ◽  
Vol 9 ◽  
Author(s):  
Jinglun Liang ◽  
Guoliang Ye ◽  
Jianwen Guo ◽  
Qifan Huang ◽  
Shaohui Zhang
Keyword(s):  
Lung Cancer ◽  
Early Stage ◽  
Pulmonary Nodules ◽  
Ct Images ◽  
False Positives ◽  
Classification Systems ◽  
Cad System ◽  
Nodule Detection ◽  
Diagnosis Of Lung Cancer ◽  
Lung Ct

Malignant pulmonary nodules are one of the main manifestations of lung cancer in early CT image screening. Since lung cancer may have no early obvious symptoms, it is important to develop a computer-aided detection (CAD) system to assist doctors to detect the malignant pulmonary nodules in the early stage of lung cancer CT diagnosis. Due to the recent successful applications of deep learning in image processing, more and more researchers have been trying to apply it to the diagnosis of pulmonary nodules. However, due to the ratio of nodules and non-nodules samples used in the training and testing datasets usually being different from the practical ratio of lung cancer, the CAD classification systems may easily produce higher false-positives while using this imbalanced dataset. This work introduces a filtering step to remove the irrelevant images from the dataset, and the results show that the false-positives can be reduced and the accuracy can be above 98%. There are two steps in nodule detection. Firstly, the images with pulmonary nodules are screened from the whole lung CT images of the patients. Secondly, the exact locations of pulmonary nodules will be detected using Faster R-CNN. Final results show that this method can effectively detect the pulmonary nodules in the CT images and hence potentially assist doctors in the early diagnosis of lung cancer.

scholarly journals RONI Based Secured and Authenticated Indexing of Lung CT Images

2015 ◽  
Vol 2015 ◽  
pp. 1-9
Author(s):  
I. Jasmine Selvakumari Jeya ◽  
J. Suganthi
Keyword(s):  
Ct Scan ◽  
Patient Information ◽  
Image Data ◽  
Ct Images ◽  
Ct Image ◽  
Secret Key ◽  
Unauthorized Access ◽  
Patient’S Information ◽  
Information Authentication ◽  
Lung Ct

Medical images need to be transmitted with the patient’s information without altering the image data. The present paper discusses secured indexing of lung CT image (SILI) which is a secured way of indexing the lung CT images with the patient information. Authentication is provided using the sender’s logo information and the secret key is used for embedding the watermark into the host image. Watermark is embedded into the region of Noninterest (RONI) of the lung CT image. RONI is identified by segmenting the lung tissue from the CT scan image. The experimental results show that the proposed approach is robust against unauthorized access, noise, blurring, and intensity based attacks.

CNN-based Prediction of COVID-19 using Chest CT Images

2021 ◽  
pp. 2250039
Author(s):  
Tanvi Arora
Keyword(s):  
Ct Scan ◽  
Good Accuracy ◽  
Ct Images ◽  
Chest Ct ◽  
X Ray ◽  
Infected People ◽  
The World ◽  
Severity Of The Disease ◽  
Application Programs ◽  
Chest Ct Scan

The coronavirus disease (COVID-19) pandemic that is caused by the SARS-CoV2 has spread all over the world. It is an infectious disease that can spread from person to person. The severity of the disease can be categorized into five categories namely asymptomatic, mild, moderate, severe, and critical. From the reported cases thus, it has been seen that 80% of the cases that test positive with COVID-19 infection have less than moderate complications, whereas 20% of the positive cases develop severe and critical complications. The virus infects the lungs of an individual, therefore, it has been observed that the X-ray and computed tomography (CT) scan images of the infected people can be used by the machine learning-based application programs to predict the presence of the infection. Therefore, in the proposed work, a Convolutional Neural Network model based upon the DenseNet architecture is being used to predict the presence of COVID-19 infection using the CT scan images of the chest. The proposed work has been carried out using the dataset of the CT images from the COVID CT Dataset. It has 349 images marked as COVID-19 positive and 397 images have been marked as COVID-19 negative. The proposed system can categorize the test set images with an accuracy of 91.4%. The proposed method is capable of detecting the presence of COVID-19 infection with good accuracy using the chest CT scan images of the humans.

AUTOMATIC DETECTION AND GRADING OF SEVERITY LEVEL IN EXUDATIVE MACULOPATHY

2011 ◽  
Vol 23 (03) ◽  
pp. 173-179 ◽  
Author(s):  
P. C. Siddalingaswamy ◽  
K. Gopalakrishna Prabhu ◽  
Vikram Jain
Keyword(s):  
Vision Loss ◽  
Early Stage ◽  
Retinal Images ◽  
Diabetic Maculopathy ◽  
Severity Level ◽  
Manual Intervention ◽  
Automated Method ◽  
The World ◽  
Severe Vision Loss ◽  
Different Levels

Diabetic maculopathy is one of the complications of diabetes mellitus that is considered as one of the major cause of vision loss among people around the world. Compulsory screening of eye will help to identify the maculopathy at early stage and reduce the risk of severe vision loss. A new automated method for the detection and grading of diabetic maculopathy severity level without any manual intervention is presented. Based on the location of exudates in marked macular region the severity level is classified into mild, moderate and severe. Digital color retinal images at different levels of maculopathy were used to evaluate the method. An overall sensitivity of 95.6% and specificity of 96.15% were achieved by the method.

scholarly journals Drawing Insights from COVID-19 Infected Patients With no Past Medical History Using CT Scan Images and Machine Learning Techniques: A Study on 200 Patients

2020 ◽  
Author(s):  
Sachin Sharma
Keyword(s):  
Machine Learning ◽  
Ct Scan ◽  
Medical History ◽  
Early Stage ◽  
Machine Learning Techniques ◽  
Past Medical History ◽  
Medical Problems ◽  
Learning Techniques ◽  
Lung Ct ◽  
Lung Ct Scan

Abstract As the whole world is witnessing what novel Coronavirus (COVID-19) can do to the mankind, it presents several unique features also. In absence of specific vaccine for COVID-19, it is essential to detect the disease at an early stage and isolate an infected patient. Till today there is a global shortage of testing labs and testing kits for COVID-19. This paper discusses about the role of machine learning techniques for getting important insights like whether lung Computed Tomography (CT) scan should be the first screening /alternative test for real-time reverse transcriptase-polymerase chain reaction (RT-PCR), Is COVID-19 pneumonia different from other viral pneumonia and if yes how to distinguish it using lung CT scan images from the carefully selected data of lung CT scan COVID-19 infected patients from the hospitals of Italy, China and India having no past medical illnesses i.e. high blood pressure, diabetes and heart disease. The reason for selecting images of the patients having no past illnesses history is that people with medical problems are already known to develop serious illness because of COVID-19 but we wanted to check and analyse condition of those patients (lung condition) which don’t have past medical history using CT scan images.

Connection of discontinuous segments in early functional recovery from thoracic ossification of the posterior longitudinal ligament treated with posterior instrumented surgery

2020 ◽  
Vol 32 (2) ◽  
pp. 200-206
Author(s):  
Kei Ando ◽  
Kazuyoshi Kobayashi ◽  
Masaaki Machino ◽  
Kyotaro Ota ◽  
Satoshi Tanaka ◽  
...  
Keyword(s):  
Early Stage ◽  
Morphological Changes ◽  
Japanese Orthopaedic Association ◽  
Multislice Ct ◽  
Posterior Longitudinal Ligament ◽  
Ct Images ◽  
Rigid Fixation ◽  
Disc Space ◽  
Fusion Surgery ◽  
Estimated Blood Loss

OBJECTIVEThe objective of this study was to investigate the relationship between morphological changes in thoracic ossification of the posterior longitudinal ligament (T-OPLL) and postoperative neurological recovery after thoracic posterior fusion surgery. Changes of OPLL morphology and postoperative recovery in cases with T-OPLL have not been examined.METHODSIn this prospective study, the authors evaluated data from 44 patients (23 male and 21 female) who underwent posterior decompression and fusion surgery with instrumentation for the treatment of T-OPLL at our hospital. The patients’ mean age at surgery was 50.7 years (range 38–68 years). The minimum duration of follow-up was 2 years. The location of thoracic ossification of the ligamentum flavum (T-OLF), T-OLF at the OPLL level, OPLL morphology, fusion range, estimated blood loss, operative time, pre- and postoperative Japanese Orthopaedic Association (JOA) scores, and JOA recovery rate were investigated. Reconstructed sagittal multislice CT images were obtained before and at 3 and 6 months and 1 and 2 years after surgery. The basic fusion area was 3 vertebrae above and below the OPLL lesion. All parameters were compared between patients with and without continuity across the disc space at the OPLL at 3 and 6 months after surgery.RESULTSThe preoperative morphology of OPLL was discontinuous across the disc space between the rostral and caudal ossification regions on sagittal CT images in all but one of the patients. Postoperatively, these segments became continuous in 42 patients (97.7%; occurring by 6.6 months on average) without progression of OPLL thickness. Patients with continuity at 3 months had significantly lower rates of diabetes mellitus (p < 0.05) and motor palsy in the lower extremities (p < 0.01). The group with continuity also had significantly higher mean postoperative JOA scores at 3 (p < 0.01) and 6 (p < 0.05) months and mean JOA recovery rates at 3 and 6 months (both p < 0.01) after surgery.CONCLUSIONSPreoperatively, discontinuity of rostral and caudal ossified lesions was found on CT in all patients but one of this group of 44 patients who needed surgery for T-OPLL. Rigid fixation with instrumentation may have allowed these segments to connect at the OPLL. Such OPLL continuity at an early stage after surgery may accelerate spinal cord recovery.

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