pt-sk/ml_research_abs huggingface dataset:
A Deep Learning based Solution (Covi-DeteCT) Amidst COVID-19.
Authors: KavitaPandey
Journal: Current medical imaging

The whole world has been severely affected due to the COVID-19 pandemic. The rapid and large-scale spread has caused immense pressure on the medical sector hence increasing the chances of false detection due to human errors and mishandling of reports. At the time of outbreaks of COVID-19, there is a crucial shortage of test kits as well. Quick diagnostic testing has become one of the main challenges. For the detection of COVID-19, many Artificial Intelligence based methodologies have been proposed, a few had suggested integration of the model on a public usable platform, but none had executed this on a working application as per our knowledge.
Keeping the above comprehension in mind, the objective is to provide an easy-to-use platform for COVID-19 identification. This work would be a contribution to the digitization of health facilities. This work is a fusion of deep learning classifiers and medical images to provide a speedy and accurate identification of the COVID-19 virus by analyzing the user's CT scan images of the lungs. It will assist healthcare workers in reducing their workload and decreasing the possibility of false detection.
In this work, various models like Resnet50V2 and Resnet101V2, an adjusted rendition of ResNet101V2 with Feature Pyramid Network, have been applied for classifying the CT scan images into the categories: normal or COVID-19 positive.
A detailed analysis of all three models' performances have been done on the SARS-CoV-2 dataset with various metrics like precision, recall, F1-score, ROC curve, etc. It was found that Resnet50V2 achieves an accuracy of 96.79%, whereas Resnet101V2 achieves an accuracy of 97.79%. An accuracy of 98.19% has been obtained by ResNet101V2 with Feature Pyramid Network. As Res- Net101V2 with Feature Pyramid Network is showing better results, thus, it is further incorporated into a working application that takes CT images as input from the user and feeds into the trained model and detects the presence of COVID-19 infection.
A mobile application integrated with the deeper variant of ResNet, i.e., ResNet101V2 with FPN checks the presence of COVID-19 in a faster and accurate manner. People can use this application on their smart mobile devices. This automated system would assist healthcare workers as well, which ultimately reduces their workload and decreases the possibility of false detection.
IEViT: An enhanced vision transformer architecture for chest X-ray image classification.
Authors: Gabriel IluebeOkolo, StamosKatsigiannis, NaeemRamzan
Journal: Computer methods and programs in biomedicine

Chest X-ray imaging is a relatively cheap and accessible diagnostic tool that can assist in the diagnosis of various conditions, including pneumonia, tuberculosis, COVID-19, and others. However, the requirement for expert radiologists to view and interpret chest X-ray images can be a bottleneck, especially in remote and deprived areas. Recent advances in machine learning have made possible the automated diagnosis of chest X-ray scans. In this work, we examine the use of a novel Transformer-based deep learning model for the task of chest X-ray image classification.
We first examine the performance of the Vision Transformer (ViT) state-of-the-art image classification machine learning model for the task of chest X-ray image classification, and then propose and evaluate the Input Enhanced Vision Transformer (IEViT), a novel enhanced Vision Transformer model that can achieve improved performance on chest X-ray images associated with various pathologies.
Experiments on four chest X-ray image data sets containing various pathologies (tuberculosis, pneumonia, COVID-19) demonstrated that the proposed IEViT model outperformed ViT for all the data sets and variants examined, achieving an F1-score between 96.39% and 100%, and an improvement over ViT of up to +5.82% in terms of F1-score across the four examined data sets. IEViT's maximum sensitivity (recall) ranged between 93.50% and 100% across the four data sets, with an improvement over ViT of up to +3%, whereas IEViT's maximum precision ranged between 97.96% and 100% across the four data sets, with an improvement over ViT of up to +6.41%.
Results showed that the proposed IEViT model outperformed all ViT's variants for all the examined chest X-ray image data sets, demonstrating its superiority and generalisation ability. Given the relatively low cost and the widespread accessibility of chest X-ray imaging, the use of the proposed IEViT model can potentially offer a powerful, but relatively cheap and accessible method for assisting diagnosis using chest X-ray images.
E-GCS: Detection of COVID-19 through classification by attention bottleneck residual network.
Authors: TAhila, A CSubhajini
Journal: Engineering applications of artificial intelligence

Recently,  the coronavirus disease 2019 (COVID-19)  has caused mortality of many people globally. Thus, there existed a need to detect this disease to prevent its further spread. Hence, the study aims to predict COVID-19 infected patients based on deep learning (DL) and image processing.
The study intends to classify the normal and abnormal cases of COVID-19 by considering three different medical imaging modalities namely ultrasound imaging, X-ray images and CT scan images through introduced attention bottleneck residual network (AB-ResNet). It also aims to segment the abnormal infected area from normal images for localizing localising the disease infected area through the proposed edge based graph cut segmentation (E-GCS).
AB-ResNet is used for classifying images whereas E-GCS segment the abnormal images. The study possess various advantages as it rely on DL and possess capability for accelerating the training speed of deep networks. It also enhance the network depth leading to minimum parameters, minimising the impact of vanishing gradient issue and attaining effective network performance with respect to better accuracy.
Performance and comparative analysis is undertaken to evaluate the efficiency of the introduced system and results explores the efficiency of the proposed system in COVID-19 detection with high accuracy (99%).
Application of Deep Learning Techniques in Diagnosis of Covid-19 (Coronavirus): A Systematic Review.
Authors: Yogesh HBhosale, K SridharPatnaik
Journal: Neural processing letters

Covid-19 is now one of the most incredibly intense and severe illnesses of the twentieth century. Covid-19 has already endangered the lives of millions of people worldwide due to its acute pulmonary effects. Image-based diagnostic techniques like X-ray, CT, and ultrasound are commonly employed to get a quick and reliable clinical condition. Covid-19 identification out of such clinical scans is exceedingly time-consuming, labor-intensive, and susceptible to silly intervention. As a result, radiography imaging approaches using Deep Learning (DL) are consistently employed to achieve great results. Various artificial intelligence-based systems have been developed for the early prediction of coronavirus using radiography pictures. Specific DL methods such as CNN and RNN noticeably extract extremely critical characteristics, primarily in diagnostic imaging. Recent coronavirus studies have used these techniques to utilize radiography image scans significantly. The disease, as well as the present pandemic, was studied using public and private data. A total of 64 pre-trained and custom DL models concerning imaging modality as taxonomies are selected from the studied articles. The constraints relevant to DL-based techniques are the sample selection, network architecture, training with minimal annotated database, and security issues. This includes evaluating causal agents, pathophysiology, immunological reactions, and epidemiological illness. DL-based Covid-19 detection systems are the key focus of this review article. Covid-19 work is intended to be accelerated as a result of this study.
Analysis of the Causes of Solitary Pulmonary Nodule Misdiagnosed as Lung Cancer by Using Artificial Intelligence: A Retrospective Study at a Single Center.
Authors: Xiong-YingWu, FanDing, KunLi, Wen-CaiHuang, YongZhang, JianZhu
Journal: Diagnostics (Basel, Switzerland)

Artificial intelligence (AI) adopting deep learning technology has been widely used in the med-ical imaging domain in recent years. It realized the automatic judgment of benign and malig-nant solitary pulmonary nodules (SPNs) and even replaced the work of doctors to some extent. However, misdiagnoses can occur in certain cases. Only by determining the causes can AI play a larger role. A total of 21 Coronavirus disease 2019 (COVID-19) patients were diagnosed with SPN by CT imaging. Their Clinical data, including general condition, imaging features, AI re-ports, and outcomes were included in this retrospective study. Although they were confirmed COVID-19 by testing reverse transcription-polymerase chain reaction (RT-PCR) with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), their CT imaging data were misjudged by AI to be high-risk nodules for lung cancer. Imaging characteristics included burr sign (76.2%), lobulated sign (61.9%), pleural indentation (42.9%), smooth edges (23.8%), and cavity (14.3%). The accuracy of AI was different from that of radiologists in judging the nature of be-nign SPNs (p < 0.001, κ = 0.036 < 0.4, means the two diagnosis methods poor fit). COVID-19 patients with SPN might have been misdiagnosed using the AI system, suggesting that the AI system needs to be further optimized, especially in the event of a new disease outbreak.
Segmentation-Based Classification Deep Learning Model Embedded with Explainable AI for COVID-19 Detection in Chest X-ray Scans.
Authors: NoneNillmani, NeerajSharma, LucaSaba, Narendra NKhanna, Mannudeep KKalra, Mostafa MFouda, Jasjit SSuri
Journal: Diagnostics (Basel, Switzerland)

Background and Motivation: COVID-19 has resulted in a massive loss of life during the last two years. The current imaging-based diagnostic methods for COVID-19 detection in multiclass pneumonia-type chest X-rays are not so successful in clinical practice due to high error rates. Our hypothesis states that if we can have a segmentation-based classification error rate <5%, typically adopted for 510 (K) regulatory purposes, the diagnostic system can be adapted in clinical settings. Method: This study proposes 16 types of segmentation-based classification deep learning-based systems for automatic, rapid, and precise detection of COVID-19. The two deep learning-based segmentation networks, namely UNet and UNet+, along with eight classification models, namely VGG16, VGG19, Xception, InceptionV3, Densenet201, NASNetMobile, Resnet50, and MobileNet, were applied to select the best-suited combination of networks. Using the cross-entropy loss function, the system performance was evaluated by Dice, Jaccard, area-under-the-curve (AUC), and receiver operating characteristics (ROC) and validated using Grad-CAM in explainable AI framework. Results: The best performing segmentation model was UNet, which exhibited the accuracy, loss, Dice, Jaccard, and AUC of 96.35%, 0.15%, 94.88%, 90.38%, and 0.99 (p-value <0.0001), respectively. The best performing segmentation-based classification model was UNet+Xception, which exhibited the accuracy, precision, recall, F1-score, and AUC of 97.45%, 97.46%, 97.45%, 97.43%, and 0.998 (p-value <0.0001), respectively. Our system outperformed existing methods for segmentation-based classification models. The mean improvement of the UNet+Xception system over all the remaining studies was 8.27%. Conclusion: The segmentation-based classification is a viable option as the hypothesis (error rate <5%) holds true and is thus adaptable in clinical practice.
Identification of micro- and nanoplastics released from medical masks using hyperspectral imaging and deep learning.
Authors: IlnurIshmukhametov, SvetlanaBatasheva, RawilFakhrullin
Journal: The Analyst

Apart from other severe consequences, the COVID-19 pandemic has inflicted a surge in personal protective equipment usage, some of which, such as medical masks, have a short effective protection time. Their misdisposition and subsequent natural degradation make them huge sources of micro- and nanoplastic particles. To better understand the consequences of the direct influence of microplastic pollution on biota, there is an urgent need to develop a reliable and high-throughput analytical tool for sub-micrometre plastic identification and visualisation in environmental and biological samples. This study evaluated the application of a combined technique based on dark-field enhanced microscopy and hyperspectral imaging augmented with deep learning data analysis for the visualisation, detection and identification of microplastic particles released from commercially available medical masks after 192 hours of UV-C irradiation. The analysis was performed using a separated blue-coloured spunbond outer layer and white-coloured meltblown interlayer that allowed us to assess the influence of the structure and pigmentation of intact and UV-exposed samples on classification performance. Microscopy revealed strong fragmentation of both layers and the formation of microparticles and fibres of various shapes after UV exposure. Based on the spectral signatures of both layers, it was possible to identify intact materials using a convolutional neural network successfully. However, the further classification of UV-exposed samples demonstrated that the spectral characteristics of samples in the visible to near-infrared range are disrupted, causing a decreased performance of the CNN. Despite this, the application of a deep learning algorithm in hyperspectral analysis outperformed the conventional spectral angle mapper technique in classifying both intact and UV-exposed samples, confirming the potential of the proposed approach in secondary microplastic analysis.
Automated Lung Segmentation from Computed Tomography Images of Normal and COVID-19 Pneumonia Patients.
Authors: FaezeGholamiankhah, SamanehMostafapour, NouraddinAbdi Goushbolagh, SeyedjafarShojaerazavi, ParvanehLayegh, Seyyed MohammadTabatabaei, HosseinArabi
Journal: Iranian journal of medical sciences

Automated image segmentation is an essential step in quantitative image analysis. This study assesses the performance of a deep learning-based model for lung segmentation from computed tomography (CT) images of normal and COVID-19 patients.
A descriptive-analytical study was conducted from December 2020 to April 2021 on the CT images of patients from various educational hospitals affiliated with Mashhad University of Medical Sciences (Mashhad, Iran). Of the selected images and corresponding lung masks of 1,200 confirmed COVID-19 patients, 1,080 were used to train a residual neural network. The performance of the residual network (ResNet) model was evaluated on two distinct external test datasets, namely the remaining 120 COVID-19 and 120 normal patients. Different evaluation metrics such as Dice similarity coefficient (DSC), mean absolute error (MAE), relative mean Hounsfield unit (HU) difference, and relative volume difference were calculated to assess the accuracy of the predicted lung masks. The Mann-Whitney U test was used to assess the difference between the corresponding values in the normal and COVID-19 patients. P<0.05 was considered statistically significant.
The ResNet model achieved a DSC of 0.980 and 0.971 and a relative mean HU difference of -2.679% and -4.403% for the normal and COVID-19 patients, respectively. Comparable performance in lung segmentation of normal and COVID-19 patients indicated the model's accuracy for identifying lung tissue in the presence of COVID-19-associated infections. Although a slightly better performance was observed in normal patients.
The ResNet model provides an accurate and reliable automated lung segmentation of COVID-19 infected lung tissue.A preprint version of this article was published on arXiv before formal peer review (https://arxiv.org/abs/2104.02042).
Detection of COVID-19 Infection in CT and X-ray images using transfer learning approach.
Authors: AlokTiwari, SumitTripathi, Dinesh ChandraPandey, NeerajSharma, ShiruSharma
Journal: Technology and health care : official journal of the European Society for Engineering and Medicine

The infection caused by the SARS-CoV-2 (COVID-19) pandemic is a threat to human lives. An early and accurate diagnosis is necessary for treatment.
The study presents an efficient classification methodology for precise identification of infection caused by COVID-19 using CT and X-ray images.
The depthwise separable convolution-based model of MobileNet V2 was exploited for feature extraction. The features of infection were supplied to the SVM classifier for training which produced accurate classification results.
The accuracies for CT and X-ray images are 99.42% and 98.54% respectively. The MCC score was used to avoid any mislead caused by accuracy and F1 score as it is more mathematically balanced metric. The MCC scores obtained for CT and X-ray were 0.9852 and 0.9657, respectively. The Youden's index showed a significant improvement of more than 2% for both imaging techniques.
The proposed transfer learning-based approach obtained the best results for all evaluation metrics and produced reliable results for the accurate identification of COVID-19 symptoms. This study can help in reducing the time in diagnosis of the infection.
A Novel Method for COVID-19 Detection Based on DCNNs and Hierarchical Structure.
Authors: YuqinLi, KeZhang, WeiliShi, ZhengangJiang
Journal: Computational and mathematical methods in medicine

The worldwide outbreak of the new coronavirus disease (COVID-19) has been declared a pandemic by the World Health Organization (WHO). It has a devastating impact on daily life, public health, and global economy. Due to the highly infectiousness, it is urgent to early screening of suspected cases quickly and accurately. Chest X-ray medical image, as a diagnostic basis for COVID-19, arouses attention from medical engineering. However, due to small lesion difference and lack of training data, the accuracy of detection model is insufficient. In this work, a transfer learning strategy is introduced to hierarchical structure to enhance high-level features of deep convolutional neural networks. The proposed framework consisting of asymmetric pretrained DCNNs with attention networks integrates various information into a wider architecture to learn more discriminative and complementary features. Furthermore, a novel cross-entropy loss function with a penalty term weakens misclassification. Extensive experiments are implemented on the COVID-19 dataset. Compared with the state-of-the-arts, the effectiveness and high performance of the proposed method are demonstrated.
Lung image segmentation based on DRD U-Net and combined WGAN with Deep Neural Network.
Authors: LuoyuLian, XinLuo, CanyuPan, JinlongHuang, WenshanHong, ZhendongXu
Journal: Computer methods and programs in biomedicine

COVID-19 is a hot issue right now, and it's causing a huge number of infections in people, posing a grave threat to human life. Deep learning-based image diagnostic technology can effectively enhance the deficiencies of the current main detection method. This paper proposes a multi-classification model diagnosis based on segmentation and classification multi-task.
In the segmentation task, the end-to-end DRD U-Net model is used to segment the lung lesions to improve the ability of feature reuse and target segmentation. In the classification task, the model combined with WGAN and Deep Neural Network classifier is used to effectively solve the problem of multi-classification of COVID-19 images with small samples, to achieve the goal of effectively distinguishing COVID-19 patients, other pneumonia patients, and normal subjects.
Experiments are carried out on common X-ray image and CT image data sets. The results display that in the segmentation task, the model is optimal in the key indicators of DSC and HD, and the error is increased by 0.33% and reduced by 3.57 mm compared with the original network U-Net. In the classification task, compared with SMOTE oversampling method, accuracy increased from 65.32% to 73.84%, F-measure increased from 67.65% to 74.65%, G-mean increased from 66.52% to 74.37%. At the same time, compared with other classical multi-task models, the results also have some advantages.
This study provides new possibilities for COVID-19 image diagnosis methods, improves the accuracy of diagnosis, and hopes to provide substantial help for COVID-19 diagnosis.
Detection of COVID-19 in Point of Care Lung Ultrasound.
Authors: JoanaMaximino, MiguelCoimbra, JoaoPedrosa
Journal: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

The coronavirus disease 2019 (COVID-19) evolved into a global pandemic, responsible for a significant number of infections and deaths. In this scenario, point-of-care ultrasound (POCUS) has emerged as a viable and safe imaging modality. Computer vision (CV) solutions have been proposed to aid clinicians in POCUS image interpretation, namely detection/segmentation of structures and image/patient classification but relevant challenges still remain. As such, the aim of this study is to develop CV algorithms, using Deep Learning techniques, to create tools that can aid doctors in the diagnosis of viral and bacterial pneumonia (VP and BP) through POCUS exams. To do so, convolutional neural networks were designed to perform in classification tasks. The architectures chosen to build these models were the VGG16, ResNet50, DenseNet169 e MobileNetV2. Patients images were divided in three classes: healthy (HE), BP and VP (which includes COVID-19). Through a comparative study, which was based on several performance metrics, the model based on the DenseNet169 architecture was designated as the best performing model, achieving 78% average accuracy value of the five iterations of 5- Fold Cross-Validation. Given that the currently available POCUS datasets for COVID-19 are still limited, the training of the models was negatively affected by such and the models were not tested in an independent dataset. Furthermore, it was also not possible to perform lesion detection tasks. Nonetheless, in order to provide explainability and understanding of the models, Gradient-weighted Class Activation Mapping (GradCAM) were used as a tool to highlight the most relevant classification regions. Clinical relevance - Reveals the potential of POCUS to support COVID-19 screening. The results are very promising although the dataset is limite.