pt-sk/ml_research_abs huggingface dataset:
Lung Disease Classification in CXR Images Using Hybrid Inception-ResNet-v2 Model and Edge Computing.
Authors: Chandra ManiSharma, LakshayGoyal, Vijayaraghavan MChariar, NavelSharma
Journal: Journal of healthcare engineering

Chest X-ray (CXR) imaging is one of the most widely used and economical tests to diagnose a wide range of diseases. However, even for expert radiologists, it is a challenge to accurately diagnose diseases from CXR samples. Furthermore, there remains an acute shortage of trained radiologists worldwide. In the present study, a range of machine learning (ML), deep learning (DL), and transfer learning (TL) approaches have been evaluated to classify diseases in an openly available CXR image dataset. A combination of the synthetic minority over-sampling technique (SMOTE) and weighted class balancing is used to alleviate the effects of class imbalance. A hybrid Inception-ResNet-v2 transfer learning model coupled with data augmentation and image enhancement gives the best accuracy. The model is deployed in an edge environment using Amazon IoT Core to automate the task of disease detection in CXR images with three categories, namely pneumonia, COVID-19, and normal. Comparative analysis has been given in various metrics such as precision, recall, accuracy, AUC-ROC score, etc. The proposed technique gives an average accuracy of 98.66%. The accuracies of other TL models, namely SqueezeNet, VGG19, ResNet50, and MobileNetV2 are 97.33%, 91.66%, 90.33%, and 76.00%, respectively. Further, a DL model, trained from scratch, gives an accuracy of 92.43%. Two feature-based ML classification techniques, namely support vector machine with local binary pattern (SVM + LBP) and decision tree with histogram of oriented gradients (DT + HOG) yield an accuracy of 87.98% and 86.87%, respectively.
Machine Learning with Quantum Seagull Optimization Model for COVID-19 Chest X-Ray Image Classification.
Authors: MahmoudRagab, SamahAlshehri, Nabil AAlhakamy, WafaaAlsaggaf, Hani AAlhadrami, JaberAlyami
Journal: Journal of healthcare engineering

Early and accurate detection of COVID-19 is an essential process to curb the spread of this deadly disease and its mortality rate. Chest radiology scan is a significant tool for early management and diagnosis of COVID-19 since the virus targets the respiratory system. Chest X-ray (CXR) images are highly useful in the effective detection of COVID-19, thanks to its availability, cost-effective means, and rapid outcomes. In addition, Artificial Intelligence (AI) techniques such as deep learning (DL) models play a significant role in designing automated diagnostic processes using CXR images. With this motivation, the current study presents a new Quantum Seagull Optimization Algorithm with DL-based COVID-19 diagnosis model, named QSGOA-DL technique. The proposed QSGOA-DL technique intends to detect and classify COVID-19 with the help of CXR images. In this regard, the QSGOA-DL technique involves the design of EfficientNet-B4 as a feature extractor, whereas hyperparameter optimization is carried out with the help of QSGOA technique. Moreover, the classification process is performed by a multilayer extreme learning machine (MELM) model. The novelty of the study lies in the designing of QSGOA for hyperparameter optimization of the EfficientNet-B4 model. An extensive series of simulations was carried out on the benchmark test CXR dataset, and the results were assessed under different aspects. The simulation results demonstrate the promising performance of the proposed QSGOA-DL technique compared to recent approaches.
A privacy-aware method for COVID-19 detection in chest CT images using lightweight deep conventional neural network and blockchain.
Authors: ArashHeidari, ShivaToumaj, Nima JafariNavimipour, MehmetUnal
Journal: Computers in biology and medicine

With the global spread of the COVID-19 epidemic, a reliable method is required for identifying COVID-19 victims. The biggest issue in detecting the virus is a lack of testing kits that are both reliable and affordable. Due to the virus's rapid dissemination, medical professionals have trouble finding positive patients. However, the next real-life issue is sharing data with hospitals around the world while considering the organizations' privacy concerns. The primary worries for training a global Deep Learning (DL) model are creating a collaborative platform and personal confidentiality. Another challenge is exchanging data with health care institutions while protecting the organizations' confidentiality. The primary concerns for training a universal DL model are creating a collaborative platform and preserving privacy. This paper provides a model that receives a small quantity of data from various sources, like organizations or sections of hospitals, and trains a global DL model utilizing blockchain-based Convolutional Neural Networks (CNNs). In addition, we use the Transfer Learning (TL) technique to initialize layers rather than initialize randomly and discover which layers should be removed before selection. Besides, the blockchain system verifies the data, and the DL method trains the model globally while keeping the institution's confidentiality. Furthermore, we gather the actual and novel COVID-19 patients. Finally, we run extensive experiments utilizing Python and its libraries, such as Scikit-Learn and TensorFlow, to assess the proposed method. We evaluated works using five different datasets, including Boukan Dr. Shahid Gholipour hospital, Tabriz Emam Reza hospital, Mahabad Emam Khomeini hospital, Maragheh Dr.Beheshti hospital, and Miandoab Abbasi hospital datasets, and our technique outperform state-of-the-art methods on average in terms of precision (2.7%), recall (3.1%), F1 (2.9%), and accuracy (2.8%).
Improved-Mask R-CNN: Towards an accurate generic MSK MRI instance segmentation platform (data from the Osteoarthritis Initiative).
Authors: BanafsheFelfeliyan, AbhilashHareendranathan, GregorKuntze, Jacob LJaremko, Janet LRonsky
Journal: Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society

Objective assessment of osteoarthritis (OA) Magnetic Resonance Imaging (MRI) scans can address the limitations of the current OA assessment approaches. Detecting and extracting bone, cartilage, and joint fluid is a necessary component for the objective assessment of OA, which helps to quantify tissue characteristics such as volume and thickness. Many algorithms, based on Artificial Intelligence (AI), have been proposed over recent years for segmenting bone and soft tissues. Most of these segmentation methods suffer from the class imbalance problem, can't differentiate between the same anatomic structure, or do not support segmenting different rang of tissue sizes. Mask R-CNN is an instance segmentation framework, meaning it segments and distinct each object of interest like different anatomical structures (e.g. bone and cartilage) using a single model. In this study, the Mask R-CNN architecture was deployed to address the need for a segmentation method that is applicable to use for different tissue scales, pathologies, and MRI sequences associated with OA, without having a problem with imbalanced classes. In addition, we modified the Mask R-CNN to improve segmentation accuracy around instance edges.
A total of 500 adult knee MRI scans from the publicly available Osteoarthritis Initiative (OAI), and 97 hip MRI scans from adults with symptomatic hip OA, evaluated by two readers, were used for training and validating the network. Three specific modifications to Mask R-CNN yielded the improved-Mask R-CNN (iMaskRCNN): an additional ROIAligned block, an extra decoder block in the segmentation header, and connecting them using a skip connection. The results were evaluated using Hausdorff distance, dice score for bone and cartilage segmentation, and differences in detected volume, dice score, and coefficients of variation (CoV) for effusion segmentation.
The iMaskRCNN led to improved bone and cartilage segmentation compared to Mask RCNN as indicated with the increase in dice score from 95% to 98% for the femur, 95-97% for the tibia, 71-80% for the femoral cartilage, and 81-82% for the tibial cartilage. For the effusion detection, the dice score improved with iMaskRCNN 72% versus Mask R-CNN 71%. The CoV values for effusion detection between Reader1 and Mask R-CNN (0.33), Reader1 and iMaskRCNN (0.34), Reader2 and Mask R-CNN (0.22), Reader2 and iMaskRCNN (0.29) are close to CoV between two readers (0.21), indicating a high agreement between the human readers and both Mask R-CNN and iMaskRCNN.
Mask R-CNN and iMaskRCNN can reliably and simultaneously extract different scale articular tissues involved in OA, forming the foundation for automated assessment of OA. The iMaskRCNN results show that the modification improved the network performance around the edges.
Facilitating standardized COVID-19 suspicion prediction based on computed tomography radiomics in a multi-demographic setting.
Authors: YeshaswiniNagaraj, Gondade Jonge, AnnaAndreychenko, GabrielePresti, Matthias AFink, NikolayPavlov, Carlo CQuattrocchi, SergeyMorozov, RaymondVeldhuis, MatthijsOudkerk, Peter M Avan Ooijen
Journal: European radiology

To develop an automatic COVID-19 Reporting and Data System (CO-RADS)-based classification in a multi-demographic setting.
This multi-institutional review boards-approved retrospective study included 2720 chest CT scans (mean age, 58 years [range 18-100 years]) from Italian and Russian patients. Three board-certified radiologists from three countries assessed randomly selected subcohorts from each population and provided CO-RADS-based annotations. CT radiomic features were extracted from the selected subcohorts after preprocessing steps like lung lobe segmentation and automatic noise reduction. We compared three machine learning models, logistic regression (LR), multilayer perceptron (MLP), and random forest (RF) for the automated CO-RADS classification. Model evaluation was carried out in two scenarios, first, training on a mixed multi-demographic subcohort and testing on an independent hold-out dataset. In the second scenario, training was done on a single demography and externally validated on the other demography.
The overall inter-observer agreement for the CO-RADS scoring between the radiologists was substantial (k = 0.80). Irrespective of the type of validation test scenario, suspected COVID-19 CT scans were identified with an accuracy of 84%. SHapley Additive exPlanations (SHAP) interpretation showed that the "wavelet_(LH)_GLCM_Imc1" feature had a positive impact on COVID prediction both with and without noise reduction. The application of noise reduction improved the overall performance between the classifiers for all types.
Using an automated model based on the COVID-19 Reporting and Data System (CO-RADS), we achieved clinically acceptable performance in a multi-demographic setting. This approach can serve as a standardized tool for automated COVID-19 assessment.
• Automatic CO-RADS scoring of large-scale multi-demographic chest CTs with mean AUC of 0.93 ± 0.04. • Validation procedure resembles TRIPOD 2b and 3 categories, enhancing the quality of experimental design to test the cross-dataset domain shift between institutions aiding clinical integration. • Identification of COVID-19 pneumonia in the presence of community-acquired pneumonia and other comorbidities with an AUC of 0.92.
Applications of Artificial Intelligence in Myopia: Current and Future Directions.
Authors: ChenchenZhang, JingZhao, ZheZhu, YanxiaLi, KeLi, YuanpingWang, YajuanZheng
Journal: Frontiers in medicine

With the continuous development of computer technology, big data acquisition and imaging methods, the application of artificial intelligence (AI) in medical fields is expanding. The use of machine learning and deep learning in the diagnosis and treatment of ophthalmic diseases is becoming more widespread. As one of the main causes of visual impairment, myopia has a high global prevalence. Early screening or diagnosis of myopia, combined with other effective therapeutic interventions, is very important to maintain a patient's visual function and quality of life. Through the training of fundus photography, optical coherence tomography, and slit lamp images and through platforms provided by telemedicine, AI shows great application potential in the detection, diagnosis, progression prediction and treatment of myopia. In addition, AI models and wearable devices based on other forms of data also perform well in the behavioral intervention of myopia patients. Admittedly, there are still some challenges in the practical application of AI in myopia, such as the standardization of datasets; acceptance attitudes of users; and ethical, legal and regulatory issues. This paper reviews the clinical application status, potential challenges and future directions of AI in myopia and proposes that the establishment of an AI-integrated telemedicine platform will be a new direction for myopia management in the post-COVID-19 period.
Deep-Precognitive Diagnosis: Preventing Future Pandemics by Novel Disease Detection With Biologically-Inspired Conv-Fuzzy Network.
Authors: AviralChharia, RahulUpadhyay, VinayKumar, ChaoCheng, JingZhang, TianyangWang, MinXu
Journal: IEEE access : practical innovations, open solutions

Deep learning-based Computer-Aided Diagnosis has gained immense attention in recent years due to its capability to enhance diagnostic performance and elucidate complex clinical tasks. However, conventional supervised deep learning models are incapable of recognizing novel diseases that do not exist in the training dataset. Automated early-stage detection of novel infectious diseases can be vital in controlling their rapid spread. Moreover, the development of a conventional CAD model is only possible after disease outbreaks and datasets become available for training (viz. COVID-19 outbreak). Since novel diseases are unknown and cannot be included in training data, it is challenging to recognize them through existing supervised deep learning models. Even after data becomes available, recognizing new classes with conventional models requires a complete extensive re-training. The present study is the
Comparison and ensemble of 2D and 3D approaches for COVID-19 detection in CT images.
Authors: Sara AtitoAli Ahmed, Mehmet CanYavuz, Mehmet UmutŞen, FatihGülşen, OnurTutar, BoraKorkmazer, CesurSamancı, SabriŞirolu, RaufHamid, Ali ErgunEryürekli, ToghrulMammadov, BerrinYanikoglu
Journal: Neurocomputing

Detecting COVID-19 in computed tomography (CT) or radiography images has been proposed as a supplement to the RT-PCR test. We compare slice-based (2D) and volume-based (3D) approaches to this problem and propose a deep learning ensemble, called IST-CovNet, combining the best 2D and 3D systems with novel preprocessing and attention modules and the use of a bidirectional Long Short-Term Memory model for combining slice-level decisions. The proposed ensemble obtains 90.80% accuracy and 0.95 AUC score overall on the newly collected IST-C dataset in detecting COVID-19 among normal controls and other types of lung pathologies; and 93.69% accuracy and 0.99 AUC score on the publicly available MosMedData dataset that consists of COVID-19 scans and normal controls only. The system also obtains state-of-art results (90.16% accuracy and 0.94 AUC) on the COVID-CT-MD dataset which is only used for testing. The system is deployed at Istanbul University Cerrahpaşa School of Medicine where it is used to automatically screen CT scans of patients, while waiting for RT-PCR tests or radiologist evaluation.
Trends and hot topics in radiology, nuclear medicine and medical imaging from 2011-2021: a bibliometric analysis of highly cited papers.
Authors: ShengYan, HuitingZhang, JunWang
Journal: Japanese journal of radiology

To spotlight the trends and hot topics looming from the highly cited papers in the subject category of Radiology, Nuclear Medicine & Medical Imaging with bibliometric analysis.
Based on the Essential Science Indicators, this study employed a bibliometric method to examine the highly cited papers in the subject category of Radiology, Nuclear Medicine & Medical Imaging in Web of Science (WoS) Categories, both quantitatively and qualitatively. In total, 1325 highly cited papers were retrieved and assessed spanning from the years of 2011 to 2021. In particular, the bibliometric information of the highly cited papers based on WoS database such as the main publication venues, the most productive countries, and the top cited publications was presented. An Abstract corpus was built to help identify the most frequently explored topics. VoSviewer was used to visualize the co-occurrence networks of author keywords.
The top three active journals are Neuroimage, Radiology and IEEE T Med Imaging. The United States, Germany and England have the most influential publications. The top cited publications unrelated to COVID-19 can be grouped in three categories: recommendations or guidelines, processing software, and analysis methods. The top cited publications on COVID-19 are dominantly in China. The most frequently explored topics based on the Abstract corpus and the author keywords with the great link strengths overlap to a great extent. Specifically, phrases such as magnetic resonance imaging, deep learning, prostate cancer, chest CT, computed tomography, CT images, coronavirus disease, convolutional neural network(s) are among the most frequently mentioned.
The bibliometric analysis of the highly cited papers provided the most updated trends and hot topics which may provide insights and research directions for medical researchers and healthcare practitioners in the future.
Transfer learning with fine-tuned deep CNN ResNet50 model for classifying COVID-19 from chest X-ray images.
Authors: Md BelalHossain, S M Hasan SazzadIqbal, Md MonirulIslam, Md NasimAkhtar, Iqbal HSarker
Journal: Informatics in medicine unlocked

COVID-19 cases are putting pressure on healthcare systems all around the world. Due to the lack of available testing kits, it is impractical for screening every patient with a respiratory ailment using traditional methods (RT-PCR). In addition, the tests have a high turn-around time and low sensitivity. Detecting suspected COVID-19 infections from the chest X-ray might help isolate high-risk people before the RT-PCR test. Most healthcare systems already have X-ray equipment, and because most current X-ray systems have already been computerized, there is no need to transfer the samples. The use of a chest X-ray to prioritize the selection of patients for subsequent RT-PCR testing is the motivation of this work. Transfer learning (TL) with fine-tuning on deep convolutional neural network-based ResNet50 model has been proposed in this work to classify COVID-19 patients from the COVID-19 Radiography Database. Ten distinct pre-trained weights, trained on varieties of large-scale datasets using various approaches such as supervised learning, self-supervised learning, and others, have been utilized in this work. Our proposed
A deep learning-based framework for detecting COVID-19 patients using chest X-rays.
Authors: SohaibAsif, MingZhao, FengxiaoTang, YusenZhu
Journal: Multimedia systems

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has caused outbreaks of new coronavirus disease (COVID-19) around the world. Rapid and accurate detection of COVID-19 coronavirus is an important step in limiting the spread of the COVID-19 epidemic. To solve this problem, radiography techniques (such as chest X-rays and computed tomography (CT)) can play an important role in the early prediction of COVID-19 patients, which will help to treat patients in a timely manner. We aimed to quickly develop a highly efficient lightweight CNN architecture for detecting COVID-19-infected patients. The purpose of this paper is to propose a robust deep learning-based system for reliably detecting COVID-19 from chest X-ray images. First, we evaluate the performance of various pre-trained deep learning models (InceptionV3, Xception, MobileNetV2, NasNet and DenseNet201) recently proposed for medical image classification. Second, a lightweight shallow convolutional neural network (CNN) architecture is proposed for classifying X-ray images of a patient with a low false-negative rate. The data set used in this work contains 2,541 chest X-rays from two different public databases, which have confirmed COVID-19 positive and healthy cases. The performance of the proposed model is compared with the performance of pre-trained deep learning models. The results show that the proposed shallow CNN provides a maximum accuracy of 99.68% and more importantly sensitivity, specificity and AUC of 99.66%, 99.70% and 99.98%. The proposed model has fewer parameters and low complexity compared to other deep learning models. The experimental results of our proposed method show that it is superior to the existing state-of-the-art methods. We believe that this model can help healthcare professionals to treat COVID-19 patients through improved and faster patient screening.
Comparison of CO-RADS Scores Based on Visual and Artificial Intelligence Assessments in a Non-Endemic Area.
Authors: YoshinobuIshiwata, KentaroMiura, MayukoKishimoto, KoichiroNomura, ShungoSawamura, ShigeruMagami, MizukiIkawa, TsuneoYamashiro, DaisukeUtsunomiya
Journal: Diagnostics (Basel, Switzerland)

In this study, we first developed an artificial intelligence (AI)-based algorithm for classifying chest computed tomography (CT) images using the coronavirus disease 2019 Reporting and Data System (CO-RADS). Subsequently, we evaluated its accuracy by comparing the calculated scores with those assigned by radiologists with varying levels of experience. This study included patients with suspected SARS-CoV-2 infection who underwent chest CT imaging between February and October 2020 in Japan, a non-endemic area. For each chest CT, the CO-RADS scores, determined by consensus among three experienced chest radiologists, were used as the gold standard. Images from 412 patients were used to train the model, whereas images from 83 patients were tested to obtain AI-based CO-RADS scores for each image. Six independent raters (one medical student, two residents, and three board-certified radiologists) evaluated the test images. Intraclass correlation coefficients (ICC) and weighted kappa values were calculated to determine the inter-rater agreement with the gold standard. The mean ICC and weighted kappa were 0.754 and 0.752 for the medical student and residents (taken together), 0.851 and 0.850 for the diagnostic radiologists, and 0.913 and 0.912 for AI, respectively. The CO-RADS scores calculated using our AI-based algorithm were comparable to those assigned by radiologists, indicating the accuracy and high reproducibility of our model. Our study findings would enable accurate reading, particularly in areas where radiologists are unavailable, and contribute to improvements in patient management and workflow.