Large Covid-19 CT scans dataset from the paper: https://doi.org/10.1016/j.bspc.2021.102588

MedicalSeg is an easy-to-use 3D medical image segmentation toolkit that supports the whole segmentation process. Specially, We provide data preprocessing acceleration, high precision model on COVID-19 CT scans dataset and MRISpineSeg spine dataset, and a 3D visualization demo based on itkwidgets.

Anonymized dataset of COVID-19 cases with a focus on radiological imaging. This includes images (x-ray / ct) with extensive metadata, such as admission-, ICU-, laboratory-, and patient master-data.

A COVID-19 CT Scan Dataset Applicable in Machine Learning and Deep Learning

Due to the irregular shapes,various sizes and indistinguishable boundaries between the normal and infected tissues, it is still a challenging task to accurately segment the infected lesions of COVID-19 on CT images. In this paper, a novel segmentation scheme is proposed for the infections of COVID-19 by enhancing supervised information and fusing multi-scale feature maps of different levels based on the encoder-decoder architecture. To this end, a deep collaborative supervision (Co-supervision) scheme is proposed to guide the network learning the features of edges and semantics. More specifically, an Edge Supervised Module (ESM) is firstly designed to highlight low-level boundary features by incorporating the edge supervised information into the initial stage of down-sampling. Meanwhile, an Auxiliary Semantic Supervised Module (ASSM) is proposed to strengthen high-level semantic information by integrating mask supervised information into the later stage. Then an Attention Fusion Module (AFM) is developed to fuse multiple scale feature maps of different levels by using an attention mechanism to reduce the semantic gaps between high-level and low-level feature maps. Finally, the effectiveness of the proposed scheme is demonstrated on four various COVID-19 CT datasets. The results show that the proposed three modules are all promising. Based on the baseline (ResUnet), using ESM, ASSM, or AFM alone can respectively increase Dice metric by 1.12%, 1.95%,1.63% in our dataset, while the integration by incorporating three models together can rise 3.97%. Compared with the existing approaches in various datasets, the proposed method can obtain better segmentation performance in some main metrics, and can achieve the best generalization and comprehensive performance.

We built a large lung CT scan dataset for COVID-19 by curating data from 7 different public datasets. These datasets have been publicly used in COVID-19 diagnosis literature and proven their efficiency in deep learning applications. Therefore, the merged dataset is expected to improve the generalization ability of deep learning methods by learning from all these resources together.

'Covid-19-Dataset' contains 1252 CT scans that are positive for SARS-CoV-2 infection (COVID-19) and 1230 CT scans for patients non-infected by SARS-CoV-2, 2482 CT scans in total.

Klasifikasi COVID-19 menggunakan Filter Gabor dan CNN dengan Hyperparameter Tuning | Dataset SarsCoV2 CT-Scan | 2021

The MosMedData.Chest CT Scans with COVID-19 Related Findings. This dataset consists of lung CT scans with COVID-19 related findings, as well as without such findings.We will be using the associated radiological findings of the CT scans as labels to build a classifier to predict presence of viral pneumonia. Hence, the task is a binary classification problem.

A list of papers and datasets on X-ray or CT image based Covid-19 screening

No description available

This dataset contains 20 CT scans of patients diagnosed with COVID-19 as well as segmentations of lungs and infections made by experts.

CheXMix: A Chest X-ray Dataset Containing a Mixture of Patient X-rays and Coronal CT Projections with COVID-19 Lung Lesion Annotations

Classification of medical images in machine learning plays an important role not only for treatment and diagnosis of so many diseases but it also eliminates the manpower, time and ease the way to the clinical experts and is also one of the leading challenge nowadays as there are enormous types of datasets available publically particularly in medical purposes. Early diagnosis of COVID-19 is crucial for disease treatment and control. Compared to RT-PCR, chest CT imaging may be a more reliable, practical and rapid method to diagnose and assess COVID-19, especially in the epidemic area. This work is based on classification of CT- Scans and aims to the classification of CT’s as COVID-19 and Normal . The images are normalized first with a constant size and then the CNN model is built with three convolutional layers and finally classification is done.

Image retrieval has gained more and more relevance in the medical field, due to the accumulation of extensive collections of scans in hospitals. These images are stored in DICOM format, which must be manually annotated and may require considerable time to process by physicians. The goal of this project is trying to address this problem by considering different approaches for building a content-based medical image retrieval system and comparing their results based on classification metrics and computational time.SARS-COV-2 Ct-Scan Dataset. The dataset consists in a total of 2482 CT scans, collected from real patients in hospital from Sao Paolo, Brazil. More than half of them resulted positive for COVID-19, while the others are not infected

COVID-CT-Dataset: A CT Scan Dataset about COVID-19

Covid-19 Dataset: Chest-Xray and Chest-CT dataset

We want to create an open access database for COVID-19 cases containing chest X-ray or CT images. Kindly send us the following data at our email : fawadmsee20@gmail.com, Data required = CT/X_Ray images with age and gender information.

Using Illumination, LBP and Machine Learning techniques on COVID-CT-Dataset( COVID-19):

Lung Lobes of COVID-19 and Cancer Patients Annotationed CT Dataset

No description available

COVID20K2C-Superpixels-Dataset - Superpixel dataset created from CT scans for SARS-CoV-2 (COVID-19) infection.

Detection of COVID-19 from standard Machine Learning Algorithms using CT scan images dataset

https://data.mendeley.com/datasets/8h65ywd2jr/2 This COVID-19 dataset consists of Non-COVID and COVID cases of both X-ray and CT images. The associated dataset is augmented with different augmentation techniques to generate about 17100 X-ray and CT images. The dataset contains two main folders, one for the X-ray images, which includes two separate sub-folders of 5500 Non-COVID images and 4044 COVID images. The other folder contains the CT images. It includes two separate sub-folders of 2628 Non-COVID images and 5427 COVID images. Cite it in your research work: El-Shafai, Walid; E. Abd El-Samie, Fathi (2020), “Extensive and Augmented COVID-19 X-Ray and CT Chest Images Dataset”, Mendeley Data, v2 For any questions, do not hesitate to contact me. Regards Walid El-Shafai eng.waled.elshafai@gmail.com

image Dataset on covid-19 disease classification with X-Ray and CT Scan Images and NIFTI Data

Exploring the Application of Attention Mechanisms in Conjunction with Baseline Models on the COVID-19-CT Dataset

Utiliizing ResNet-18 (pre-trained on ImageNet dataset, fine tuned on downloaded data set) to classify Covid-19 from Chest CT images

A COVID-19 Detection Project typically involves using Machine Learning (ML) or Deep Learning (DL) techniques to diagnose COVID-19 based on medical data such as X-ray images, CT scans, or symptoms-based datasets

A web application that categorise images into covid-19 and non covid-19 cases using CNN with three pretrained models InceptionV3, VGG16 and Resnet50 on 3 different dataset of CT scan chest images. the application was built using Django/ Pycharm/ Python/ Tensorflow

From the onset of 2020, Coronavirus disease (COVID-19) has rapidly accelerated worldwide into a stage of a severe pandemic. COVID-19 has infected more than 29 million people and caused more than 900 thousand deaths. Being highly contagious, it causes community transmission explosively. Thus, health care delivery has been disrupted and compromised by lack of testing kits. The COVID-19 infected patient shows severe acute respiratory syndrome. Meanwhile, the scientific community has been on a roll implementing Deep Learning techniques to diagnose COVID- 19 based on lung CT-scans, as computed tomography (CT) is a pertinent screening tool due to its higher sensitivity for recognizing early pneumonic changes. However, large dataset of CT-scan images are not publicly available due to privacy concerns and obtaining very accurate model becomes difficult. Thus to overcome this drawback, transfer learning pre-trained models are used to classify COVID-19 (+ve) and COVID-19 (-ve) patient in the proposed methodology. Including pre-trained models (DenseNet201, VGG16, ResNet50V2, MobileNet) as backbone, a deep learning framework is developed and named as KarNet. For extensive testing analysis of the framework, each model is trained on original (i.e., non-augmented) and manipulated (i.e., augmented) dataset. Among the four pre-trained models of KarNet, the one with DenseNet201 illustrated excellent diagnostic ability with an AUC score of 1.00 and 0.99 for models trained on non-augmented and augmented data set respectively. Even after considerable distortion of images (i.e., augmented dataset) DenseNet201 gained an accuracy of 97% on the testing set, followed by ResNet50V2, MobileNet, VGG16 (96%, 95% and 94% respectively).