pt-sk/ml_research_abs huggingface dataset:
Feature-level ensemble approach for COVID-19 detection using chest X-ray images.
Authors: Thi Kieu KhanhHo, JeonghwanGwak
Journal: PloS one

Severe acute respiratory syndrome coronavirus 2 (SARS CoV-2), also known as the coronavirus disease 2019 (COVID-19), has threatened many human beings around the world and capsized economies at unprecedented magnitudes. Therefore, the detection of this disease using chest X-ray modalities has played a pivotal role in producing fast and accurate medical diagnoses, especially in countries that are unable to afford laboratory testing kits. However, identifying and distinguishing COVID-19 from virtually similar thoracic abnormalities utilizing medical images is challenging because it is time-consuming, demanding, and susceptible to human-based errors. Therefore, artificial-intelligence-driven automated diagnoses, which excludes direct human intervention, may potentially be used to achieve consistently accurate performances. In this study, we aimed to (i) obtain a customized dataset composed of a relatively small number of images collected from publicly available datasets; (ii) present the efficient integration of the shallow handcrafted features obtained from local descriptors, radiomics features specialized for medical images, and deep features aggregated from pre-trained deep learning architectures; and (iii) distinguish COVID-19 patients from healthy controls and pneumonia patients using a collection of conventional machine learning classifiers. By conducting extensive experiments, we demonstrated that the feature-based ensemble approach provided the best classification metrics, and this approach explicitly outperformed schemes that used only either local, radiomic, or deep features. In addition, our proposed method achieved state-of-the-art multi-class classification results compared to the baseline reference for the currently available COVID-19 datasets.
Classification of COVID-19 from tuberculosis and pneumonia using deep learning techniques.
Authors: LokeswariVenkataramana, D Venkata VaraPrasad, SSaraswathi, C MMithumary, RKarthikeyan, NMonika
Journal: Medical & biological engineering & computing

Deep learning provides the healthcare industry with the ability to analyse data at exceptional speeds without compromising on accuracy. These techniques are applicable to healthcare domain for accurate and timely prediction. Convolutional neural network is a class of deep learning methods which has become dominant in various computer vision tasks and is attracting interest across a variety of domains, including radiology. Lung diseases such as tuberculosis (TB), bacterial and viral pneumonias, and COVID-19 are not predicted accurately due to availability of very few samples for either of the lung diseases. The disease could be easily diagnosed using X-ray or CT scan images. But the number of images available for each of the disease is not as equally as other resulting in imbalance nature of input data. Conventional supervised machine learning methods do not achieve higher accuracy when trained using a lesser amount of COVID-19 data samples. Image data augmentation is a technique that can be used to artificially expand the size of a training dataset by creating modified versions of images in the dataset. Data augmentation helped reduce overfitting when training a deep neural network. The SMOTE (Synthetic Minority Oversampling Technique) algorithm is used for the purpose of balancing the classes. The novelty in this research work is to apply combined data augmentation and class balance techniques before classification of tuberculosis, pneumonia, and COVID-19. The classification accuracy obtained with the proposed multi-level classification after training the model is recorded as 97.4% for TB and pneumonia and 88% for bacterial, viral, and COVID-19 classifications. The proposed multi-level classification method produced is ~8 to ~10% improvement in classification accuracy when compared with the existing methods in this area of research. The results reveal the fact that the proposed system is scalable to growing medical data and classifies lung diseases and its sub-types in less time with higher accuracy.
RLMD-PA: A Reinforcement Learning-Based Myocarditis Diagnosis Combined with a Population-Based Algorithm for Pretraining Weights.
Authors: Seyed VahidMoravvej, RoohallahAlizadehsani, SadiaKhanam, ZahraSobhaninia, AfshinShoeibi, FahimeKhozeimeh, Zahra AlizadehSani, Ru-SanTan, AbbasKhosravi, SaeidNahavandi, Nahrizul AdibKadri, Muhammad MokhzainiAzizan, NArunkumar, U RajendraAcharya
Journal: Contrast media & molecular imaging

Myocarditis is heart muscle inflammation that is becoming more prevalent these days, especially with the prevalence of COVID-19. Noninvasive imaging cardiac magnetic resonance (CMR) can be used to diagnose myocarditis, but the interpretation is time-consuming and requires expert physicians. Computer-aided diagnostic systems can facilitate the automatic screening of CMR images for triage. This paper presents an automatic model for myocarditis classification based on a deep reinforcement learning approach called as reinforcement learning-based myocarditis diagnosis combined with population-based algorithm (RLMD-PA) that we evaluated using the Z-Alizadeh Sani myocarditis dataset of CMR images prospectively acquired at Omid Hospital, Tehran. This model addresses the imbalanced classification problem inherent to the CMR dataset and formulates the classification problem as a sequential decision-making process. The policy of architecture is based on convolutional neural network (CNN). To implement this model, we first apply the artificial bee colony (ABC) algorithm to obtain initial values for RLMD-PA weights. Next, the agent receives a sample at each step and classifies it. For each classification act, the agent gets a reward from the environment in which the reward of the minority class is greater than the reward of the majority class. Eventually, the agent finds an optimal policy under the guidance of a particular reward function and a helpful learning environment. Experimental results based on standard performance metrics show that RLMD-PA has achieved high accuracy for myocarditis classification, indicating that the proposed model is suitable for myocarditis diagnosis.
Non-iterative learning machine for identifying CoViD19 using chest X-ray images.
Authors: SahilDalal, Virendra PVishwakarma, VarshaSisaudia, ParulNarwal
Journal: Scientific reports

CoViD19 is a novel disease which has created panic worldwide by infecting millions of people around the world. The last significant variant of this virus, called as omicron, contributed to majority of cases in the third wave across globe. Though lesser in severity as compared to its predecessor, the delta variant, this mutation has shown higher communicable rate. This novel virus with symptoms of pneumonia is dangerous as it is communicable and hence, has engulfed entire world in a very short span of time. With the help of machine learning techniques, entire process of detection can be automated so that direct contacts can be avoided. Therefore, in this paper, experimentation is performed on CoViD19 chest X-ray images using higher order statistics with iterative and non-iterative models. Higher order statistics provide a way of analyzing the disturbances in the chest X-ray images. The results obtained are quite good with 96.64% accuracy using a non-iterative model. For fast testing of the patients, non-iterative model is preferred because it has advantage over iterative model in terms of speed. Comparison with some of the available state-of-the-art methods and some iterative methods proves efficacy of the work.
Detection of COVID-19 using deep learning techniques and classification methods.
Authors: ÇinareOğuz, MeteYağanoğlu
Journal: Information processing & management

Since the patient is not quarantined during the conclusion of the Polymerase Chain Reaction (PCR) test used in the diagnosis of COVID-19, the disease continues to spread. In this study, it was aimed to reduce the duration and amount of transmission of the disease by shortening the diagnosis time of COVID-19 patients with the use of Computed Tomography (CT). In addition, it is aimed to provide a decision support system to radiologists in the diagnosis of COVID-19. In this study, deep features were extracted with deep learning models such as ResNet-50, ResNet-101, AlexNet, Vgg-16, Vgg-19, GoogLeNet, SqueezeNet, Xception on 1345 CT images obtained from the radiography database of Siirt Education and Research Hospital. These deep features are given to classification methods such as Support Vector Machine (SVM), k Nearest Neighbor (kNN), Random Forest (RF), Decision Trees (DT), Naive Bayes (NB), and their performance is evaluated with test images. Accuracy value, F1-score and ROC curve were considered as success criteria. According to the data obtained as a result of the application, the best performance was obtained with ResNet-50 and SVM method. The accuracy was 96.296%, the F1-score was 95.868%, and the AUC value was 0.9821. The deep learning model and classification method examined in this study and found to be high performance can be used as an auxiliary decision support system by preventing unnecessary tests for COVID-19 disease.
Computational Intelligence-Based Method for Automated Identification of COVID-19 and Pneumonia by Utilizing CXR Scans.
Authors: BhavanaKaushik, DeepikaKoundal, NeelamGoel, AtefZaguia, AssayeBelay, HamzaTurabieh
Journal: Computational intelligence and neuroscience

Chest X-ray (CXR) scans are emerging as an important diagnostic tool for the early spotting of COVID and other significant lung diseases. The recognition of visual symptoms is difficult and can take longer time by radiologists as CXR provides various signs of viral infection. Therefore, artificial intelligence-based method for automated identification of COVID by utilizing X-ray images has been found to be very promising. In the era of deep learning, effective utilization of existing pretrained generalized models is playing a decisive role in terms of time and accuracy. In this paper, the benefits of weights of existing pretrained model VGG16 and InceptionV3 have been taken. Base model has been created using pretrained models (VGG16 and InceptionV3). The last fully connected (FC) layer has been added as per the number of classes for classification of CXR in binary and multi-class classification by appropriately using transfer learning. Finally, combination of layers is made by integrating the FC layer weights of both the models (VGG16 and InceptionV3). The image dataset used for experimentation consists of healthy, COVID, pneumonia viral, and pneumonia bacterial. The proposed weight fusion method has outperformed the existing models in terms of accuracy, achieved 99.5% accuracy in binary classification over 20 epochs, and 98.2% accuracy in three-class classification over 100 epochs.
PCA-Based Incremental Extreme Learning Machine (PCA-IELM) for COVID-19 Patient Diagnosis Using Chest X-Ray Images.
Authors: VinodKumar, SougatamoyBiswas, Dharmendra SinghRajput, HarshitaPatel, BasantTiwari
Journal: Computational intelligence and neuroscience

Novel coronavirus 2019 has created a pandemic and was first reported in December 2019. It has had very adverse consequences on people's daily life, healthcare, and the world's economy as well. According to the World Health Organization's most recent statistics, COVID-19 has become a worldwide pandemic, and the number of infected persons and fatalities growing at an alarming rate. It is highly required to have an effective system to early detect the COVID-19 patients to curb the further spreading of the virus from the affected person. Therefore, to early identify positive cases in patients and to support radiologists in the automatic diagnosis of COVID-19 from X-ray images, a novel method PCA-IELM is proposed based on principal component analysis (PCA) and incremental extreme learning machine. The suggested method's key addition is that it considers the benefits of PCA and the incremental extreme learning machine. Further, our strategy PCA-IELM reduces the input dimension by extracting the most important information from an image. Consequently, the technique can effectively increase the COVID-19 patient prediction performance. In addition to these, PCA-IELM has a faster training speed than a multi-layer neural network. The proposed approach was tested on a COVID-19 patient's chest X-ray image dataset. The experimental results indicate that the proposed approach PCA-IELM outperforms PCA-SVM and PCA-ELM in terms of accuracy (98.11%), precision (96.11%), recall (97.50%), F1-score (98.50%), etc., and training speed.
CAD systems for COVID-19 diagnosis and disease stage classification by segmentation of infected regions from CT images.
Authors: Mohammad HAlshayeji, SilpaChandraBhasi Sindhu, Sa'edAbed
Journal: BMC bioinformatics

Here propose a computer-aided diagnosis (CAD) system to differentiate COVID-19 (the coronavirus disease of 2019) patients from normal cases, as well as to perform infection region segmentation along with infection severity estimation using computed tomography (CT) images. The developed system facilitates timely administration of appropriate treatment by identifying the disease stage without reliance on medical professionals. So far, this developed model gives the most accurate, fully automatic COVID-19 real-time CAD framework.
The CT image dataset of COVID-19 and non-COVID-19 individuals were subjected to conventional ML stages to perform binary classification. In the feature extraction stage, SIFT, SURF, ORB image descriptors and bag of features technique were implemented for the appropriate differentiation of chest CT regions affected with COVID-19 from normal cases. This is the first work introducing this concept for COVID-19 diagnosis application. The preferred diverse database and selected features that are invariant to scale, rotation, distortion, noise etc. make this framework real-time applicable. Also, this fully automatic approach which is faster compared to existing models helps to incorporate it into CAD systems. The severity score was measured based on the infected regions along the lung field. Infected regions were segmented through a three-class semantic segmentation of the lung CT image. Using severity score, the disease stages were classified as mild if the lesion area covers less than 25% of the lung area; moderate if 25-50% and severe if greater than 50%. Our proposed model resulted in classification accuracy of 99.7% with a PNN classifier, along with area under the curve (AUC) of 0.9988, 99.6% sensitivity, 99.9% specificity and a misclassification rate of 0.0027. The developed infected region segmentation model gave 99.47% global accuracy, 94.04% mean accuracy, 0.8968 mean IoU (intersection over union), 0.9899 weighted IoU, and a mean Boundary F1 (BF) contour matching score of 0.9453, using Deepabv3+ with its weights initialized using ResNet-50.
The developed CAD system model is able to perform fully automatic and accurate diagnosis of COVID-19 along with infected region extraction and disease stage identification. The ORB image descriptor with bag of features technique and PNN classifier achieved the superior classification performance.
FWLICM-Deep Learning: Fuzzy Weighted Local Information C-Means Clustering-Based Lung Lobe Segmentation with Deep Learning for COVID-19 Detection.
Authors: RRajeswari, VeerrajuGampala, BalajeeMaram, RCristin
Journal: Journal of digital imaging

Coronavirus (COVID-19) creates an extensive range of respiratory contagions, and it is a kind of ribonucleic acid (RNA) virus, which affects both animals and humans. Moreover, COVID-19 is a new disease, which produces contamination in upper respiration alterritory and lungs. The new COVID is a rapidly spreading pathogen globally, and it threatens billions of humans' lives. However, it is significant to identify positive cases in order to avoid the spread of plague and to speedily treat infected patients. Hence, in this paper, the WSCA-based RMDL approach is devised for COVID-19 prediction by means of chest X-ray images. Moreover, Fuzzy Weighted Local Information C-Means (FWLICM) approach is devised in order to segment lung lobes. The developed FWLICM method is designed by modifying the Fuzzy Local Information C-Means (FLICM) technique. Additionally, random multimodel deep learning (RMDL) classifier is utilized for the COVID-19 prediction process. The new optimization approach, named water sine cosine algorithm (WSCA), is devised in order to obtain an effective prediction. The developed WSCA is newly designed by incorporating sine cosine algorithm (SCA) and water cycle algorithm (WCA). The developed WSCA-driven RMDL approach outperforms other COVID-19 prediction techniques with regard to accuracy, specificity, sensitivity, and dice score of 92.41%, 93.55%, 92.14%, and 90.02%.
Self-evolving vision transformer for chest X-ray diagnosis through knowledge distillation.
Authors: SangjoonPark, GwanghyunKim, YujinOh, Joon BeomSeo, Sang MinLee, Jin HwanKim, SungjunMoon, Jae-KwangLim, Chang MinPark, Jong ChulYe
Journal: Nature communications

Although deep learning-based computer-aided diagnosis systems have recently achieved expert-level performance, developing a robust model requires large, high-quality data with annotations that are expensive to obtain. This situation poses a conundrum that annually-collected chest x-rays cannot be utilized due to the absence of labels, especially in deprived areas. In this study, we present a framework named distillation for self-supervision and self-train learning (DISTL) inspired by the learning process of the radiologists, which can improve the performance of vision transformer simultaneously with self-supervision and self-training through knowledge distillation. In external validation from three hospitals for diagnosis of tuberculosis, pneumothorax, and COVID-19, DISTL offers gradually improved performance as the amount of unlabeled data increase, even better than the fully supervised model with the same amount of labeled data. We additionally show that the model obtained with DISTL is robust to various real-world nuisances, offering better applicability in clinical setting.
Development and validation of bone-suppressed deep learning classification of COVID-19 presentation in chest radiographs.
Authors: Ngo Fung DanielLam, HongfeiSun, LimingSong, DongrongYang, ShaohuaZhi, GeRen, Pak HeiChou, Shiu Bun NelsonWan, Man Fung EstherWong, King KwongChan, Hoi Ching HaileyTsang, Feng-Ming SpringKong, Yì Xiáng JWáng, JingQin, Lawrence Wing ChiChan, MichaelYing, JingCai
Journal: Quantitative imaging in medicine and surgery

Coronavirus disease 2019 (COVID-19) is a pandemic disease. Fast and accurate diagnosis of COVID-19 from chest radiography may enable more efficient allocation of scarce medical resources and hence improved patient outcomes. Deep learning classification of chest radiographs may be a plausible step towards this. We hypothesize that bone suppression of chest radiographs may improve the performance of deep learning classification of COVID-19 phenomena in chest radiographs.
Two bone suppression methods (Gusarev 
Bone suppression of external test data was found to significantly (P<0.05) improve AUC for one classifier architecture [from 0.698 (non-suppressed) to 0.732 (Rajaraman-suppressed)]. For the other classifier architecture, suppression did not significantly (P>0.05) improve or worsen classifier performance.
Rajaraman suppression significantly improved classification performance in one classification architecture, and did not significantly worsen classifier performance in the other classifier architecture. This research could be extended to explore the impact of bone suppression on classification of different lung pathologies, and the effect of other image enhancement techniques on classifier performance.
Multi-branch fusion auxiliary learning for the detection of pneumonia from chest X-ray images.
Authors: JiaLiu, JingQi, WeiChen, YongjianNian
Journal: Computers in biology and medicine

Lung infections caused by bacteria and viruses are infectious and require timely screening and isolation, and different types of pneumonia require different treatment plans. Therefore, finding a rapid and accurate screening method for lung infections is critical. To achieve this goal, we proposed a multi-branch fusion auxiliary learning (MBFAL) method for pneumonia detection from chest X-ray (CXR) images. The MBFAL method was used to perform two tasks through a double-branch network. The first task was to recognize the absence of pneumonia (normal), COVID-19, other viral pneumonia and bacterial pneumonia from CXR images, and the second task was to recognize the three types of pneumonia from CXR images. The latter task was used to assist the learning of the former task to achieve a better recognition effect. In the process of auxiliary parameter updating, the feature maps of different branches were fused after sample screening through label information to enhance the model's ability to recognize case of pneumonia without impacting its ability to recognize normal cases. Experiments show that an average classification accuracy of 95.61% is achieved using MBFAL. The single class accuracy for normal, COVID-19, other viral pneumonia and bacterial pneumonia was 98.70%, 99.10%, 96.60% and 96.80%, respectively, and the recall was 97.20%, 98.60%, 96.10% and 89.20%, respectively, using the MBFAL method. Compared with the baseline model and the model constructed using the above methods separately, better results for the rapid screening of pneumonia were achieved using MBFAL.