scholarly journals A federated approach for detecting the chest diseases using DenseNet for multi-label classification

2021 ◽  
Author(s):  
K. V. Priya ◽  
J. Dinesh Peter
Keyword(s):  
Deep Neural Networks ◽  
Early Stage ◽  
Disease Classification ◽  
Classification Algorithms ◽  
Classification Problems ◽  
Improve Performance ◽  
X Ray ◽  
Full Dataset ◽  
Proposed Model ◽  
Chest X Ray

AbstractMulti-label disease classification algorithms help to predict various chronic diseases at an early stage. Diverse deep neural networks are applied for multi-label classification problems to foresee multiple mutually non-exclusive classes or diseases. We propose a federated approach for detecting the chest diseases using DenseNets for better accuracy in prediction of various diseases. Images of chest X-ray from the Kaggle repository is used as the dataset in the proposed model. This new model is tested with both sample and full dataset of chest X-ray, and it outperforms existing models in terms of various evaluation metrics. We adopted transfer learning approach along with the pre-trained network from scratch to improve performance. For this, we have integrated DenseNet121 to our framework. DenseNets have a few focal points as they help to overcome vanishing gradient issues, boost up the feature propagation and reuse and also to reduce the number of parameters. Furthermore, gradCAMS are used as visualization methods to visualize the affected parts on chest X-ray. Henceforth, the proposed architecture will help the prediction of various diseases from a single chest X-ray and furthermore direct the doctors and specialists for taking timely decisions.

scholarly journals An Effective Convolutional Neural Network Model for the Early Detection of COVID-19 Using Chest X-ray Images

2021 ◽  
Vol 11 (21) ◽  
pp. 10301
Author(s):  
Muhammad Shoaib Farooq ◽  
Attique Ur Rehman ◽  
Muhammad Idrees ◽  
Muhammad Ahsan Raza ◽  
Jehad Ali ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Real World ◽  
Early Stage ◽  
Binary Classification ◽  
X Rays ◽  
X Ray ◽  
Proposed Model ◽  
Chest X Ray

COVID-19 has been difficult to diagnose and treat at an early stage all over the world. The numbers of patients showing symptoms for COVID-19 have caused medical facilities at hospitals to become unavailable or overcrowded, which is a major challenge. Studies have recently allowed us to determine that COVID-19 can be diagnosed with the aid of chest X-ray images. To combat the COVID-19 outbreak, developing a deep learning (DL) based model for automated COVID-19 diagnosis on chest X-ray is beneficial. In this research, we have proposed a customized convolutional neural network (CNN) model to detect COVID-19 from chest X-ray images. The model is based on nine layers which uses a binary classification method to differentiate between COVID-19 and normal chest X-rays. It provides COVID-19 detection early so the patients can be admitted in a timely fashion. The proposed model was trained and tested on two publicly available datasets. Cross-dataset studies are used to assess the robustness in a real-world context. Six hundred X-ray images were used for training and two hundred X-rays were used for validation of the model. The X-ray images of the dataset were preprocessed to improve the results and visualized for better analysis. The developed algorithm reached 98% precision, recall and f1-score. The cross-dataset studies also demonstrate the resilience of deep learning algorithms in a real-world context with 98.5 percent accuracy. Furthermore, a comparison table was created which shows that our proposed model outperforms other relative models in terms of accuracy. The quick and high-performance of our proposed DL-based customized model identifies COVID-19 patients quickly, which is helpful in controlling the COVID-19 outbreak.

An Efficient Approach to Predict COVID-19 Infected Patient Applying Deep learning Algorithm on Chest X-ray Images with Analyzing the Patient Symptoms (Preprint)

2020 ◽  
Author(s):  
Mohammad Helal Uddin ◽  
Mohammad Nahid Hossain ◽  
K. Thapa ◽  
S.-H Yang
Keyword(s):  
Learning Algorithm ◽  
Early Stage ◽  
Low Cost ◽  
Research Work ◽  
Lung Infection ◽  
X Ray ◽  
Deep Learning Algorithm ◽  
Proposed Model ◽  
Life Threatening ◽  
Chest X Ray

BACKGROUND COVID-19 is a life-threatening infectious disease that has become a pandemic for the time being. The virus grows within the lower respiratory tract where early-stage symptoms(like- cough, fever, sore throat, etc.) develop and then it causes lung infection(pneumonia) OBJECTIVE This paper proposed a new methodology of artificial testing whether a patient has been infected by COVID-19 or not METHODS We have presented a prediction model based on, Convolutional Neural Networks(CNN) and our own developed mathematical equation based algorithm named SymptomNet. The CNN algorithm classifies the lung infection(pneumonia) from frontal chest X-ray images, while the symptoms analyzing algorithm(SymptomNet) predicts the possibility of COVID-19 infection from developed symptoms in a patient RESULTS The model has the accuracy of 96% while predicting COVID-19 patients. In this Model, the CNN classifier has the accuracy of around 96% and the SymptomNet algorithm has the accuracy of 97%. CONCLUSIONS This research work obtained a promising accuracy while predicting COVID-19 infected patients. The proposed model can be ubiquitously used at a low cost with high accuracy.

scholarly journals Analysis of Separability of COVID-19 and Pneumonia in Chest X-ray Images by Means of Convolutional Neural Networks

Proceedings ◽  
2020 ◽  
Vol 54 (1) ◽  
pp. 31
Author(s):  
Joaquim de Moura ◽  
Lucía Ramos ◽  
Plácido L. Vidal ◽  
Jorge Novo ◽  
Marcos Ortega
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Clinical Symptoms ◽  
Early Stage ◽  
World Health ◽  
Complete Analysis ◽  
Screening Process ◽  
X Ray ◽  
Chest X Ray ◽  
Health Organization

The new coronavirus (COVID-19) is a disease that is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). On 11 March 2020, the coronavirus outbreak has been labelled a global pandemic by the World Health Organization. In this context, chest X-ray imaging has become a remarkably powerful tool for the identification of patients with COVID-19 infections at an early stage when clinical symptoms may be unspecific or sparse. In this work, we propose a complete analysis of separability of COVID-19 and pneumonia in chest X-ray images by means of Convolutional Neural Networks. Satisfactory results were obtained that demonstrated the suitability of the proposed system, improving the efficiency of the medical screening process in the healthcare systems.

Molluscum-Like Papules as a Presentation of Early Papulonecrotic Tuberculid in Association with Nodular Tuberculid in a Male with Asymptomatic Active Pulmonary Tuberculosis

2015 ◽  
Vol 19 (2) ◽  
pp. 159-162 ◽  
Author(s):  
Rachel Asiniwasis ◽  
Maha T. Dutil ◽  
Scott Walsh
Keyword(s):  
Pulmonary Tuberculosis ◽  
Early Stage ◽  
Induced Sputum ◽  
Simultaneous Occurrence ◽  
Antituberculous Therapy ◽  
X Ray ◽  
Active Pulmonary Tuberculosis ◽  
History Of ◽  
Chest X Ray ◽  
Histopathologic Findings

Background/Objectives The clinical and histopathologic findings of a rare simultaneous occurrence of papulonecrotic tuberculid and nodular tuberclid in a patient with active but asymptomatic pulmonary tuberculosis are presented. Papulonecrotic tuberculid was observed at a very early stage, presenting as molluscum-like lesions. This has been described once in the literature. This was observed in conjunction with lesions compatible with the rare clinicopathologic variant of nodular tuberculid. Critical to the diagnosis of active pulmonary tuberculosis was the use of induced sputum testing, which confirmed the diagnosis despite the lack of a cough and a chest x-ray negative for active tuberculosis. Methods/Results A 40-year-old male presented with a 2-week history of fever and a skin eruption consisting of molluscum-like papules on the ears, arms, and abdomen and nodules on his legs. Biopsies from both lesions were consistent with papulonecrotic and nodular tuberculid, respectively. Despite the lack of any respiratory symptoms, induced sputum grew Mycobacterium tuberculosis, and the lesions resolved on antituberculous therapy. Conclusions and Relevance Tuberculids are rare in Western countries but must be considered in the differential diagnosis of eruptions in patients from endemic countries. An active tuberculous focus must be sought out.

scholarly journals SDFN: Segmentation-based deep fusion network for thoracic disease classification in chest X-ray images

2019 ◽  
Vol 75 ◽  
pp. 66-73 ◽  
Author(s):  
Han Liu ◽  
Lei Wang ◽  
Yandong Nan ◽  
Faguang Jin ◽  
Qi Wang ◽  
...  
Keyword(s):  
Disease Classification ◽  
Thoracic Disease ◽  
X Ray ◽  
Chest X Ray

scholarly journals A Hybrid Capsule Network for Pneumonia Detection Using Image Augmentation Based on Generative Adversarial Network

2021 ◽  
Vol 38 (3) ◽  
pp. 619-627
Author(s):  
Kazim Firildak ◽  
Muhammed Fatih Talu
Keyword(s):  
Classification Accuracy ◽  
Data Augmentation ◽  
Model Parameters ◽  
Classification Problems ◽  
Generative Adversarial Network ◽  
X Ray ◽  
Discrimination Capability ◽  
Adversarial Network ◽  
Original Dataset ◽  
Chest X Ray

Pneumonia, featured by inflammation of the air sacs in one or both lungs, is usually detected by examining chest X-ray images. This paper probes into the classification models that can distinguish between normal and pneumonia images. As is known, trained networks like AlexNet and GoogleNet are deep network architectures, which are widely adopted to solve many classification problems. They have been adapted to the target datasets, and employed to classify new data generated through transfer learning. However, the classical architectures are not accurate enough for the diagnosis of pneumonia. Therefore, this paper designs a capsule network with high discrimination capability, and trains the network on Kaggle’s online pneumonia dataset, which contains chest X-ray images of many adults and children. The original dataset consists of 1,583 normal images, and 4,273 pneumonia images. Then, two data augmentation approaches were applied to the dataset, and their effects on classification accuracy were compared in details. The model parameters were optimized through five different experiments. The results show that the highest classification accuracy (93.91% even on small images) was achieved by the capsule network, coupled with data augmentation by generative adversarial network (GAN), using optimized parameters. This network outperformed the classical strategies.

scholarly journals Chest X-Ray Image Preprocessing for Disease Classification

2021 ◽  
Vol 192 ◽  
pp. 658-665
Author(s):  
Guy Caseneuve ◽  
Iren Valova ◽  
Nathan LeBlanc ◽  
Melanie Thibodeau
Keyword(s):  
Disease Classification ◽  
Image Preprocessing ◽  
X Ray ◽  
Chest X Ray

scholarly journals COVIDPEN: A Novel COVID-19 Detection Model using Chest X-Rays and CT Scans

2020 ◽  
Author(s):  
Amit Kumar Jaiswal ◽  
Prayag Tiwari ◽  
Vipin Kumar Rathi ◽  
Jia Qian ◽  
Hari Mohan Pandey ◽  
...  
Keyword(s):  
False Negative ◽  
Healthcare Providers ◽  
False Negative Rate ◽  
X Rays ◽  
X Ray ◽  
Detection Model ◽  
Global Pandemic ◽  
High False Negative Rate ◽  
Proposed Model ◽  
Chest X Ray

The trending global pandemic of COVID-19 is the fastest ever impact which caused people worldwide by severe acute respiratory syndrome~(SARS)-driven coronavirus. However, several countries suffer from the shortage of test kits and high false negative rate in PCR test. Enhancing the chest X-ray or CT detection rate becomes critical. The patient triage is of utmost importance and the use of machine learning can drive the diagnosis of chest X-ray or CT image by identifying COVID-19 cases. To tackle this problem, we propose~COVIDPEN~-~a transfer learning approach on Pruned EfficientNet-based model for the detection of COVID-19 cases. The proposed model is further interpolated by post-hoc analysis for the explainability of the predictions. The effectiveness of our proposed model is demonstrated on two systematic datasets of chest radiographs and computed tomography scans. Experimental results with several baseline comparisons show that our method is on par and confers clinically explicable instances, which are meant for healthcare providers.

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