scholarly journals Identifying drug-resistant tuberculosis from chest X-ray images using a simple convolutional neural network

2021 ◽  
Vol 2071 (1) ◽  
pp. 012001
Author(s):  
J Ureta ◽  
A Shrestha
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Binary Classification ◽  
Health Concern ◽  
Drug Resistant ◽  
X Rays ◽  
Drug Resistant Tuberculosis ◽  
X Ray ◽  
Resistant Tuberculosis ◽  
Chest X Ray

Abstract Tuberculosis(TB) is one of the top 10 causes of death worldwide, and drug-resistant TB is a major public health concern especially in resource-constrained countries. In such countries, molecular diagnosis of drug-resistant TB remains a challenge; and imaging tools such as X-rays, which are cheaply and widely available, can be a valuable supplemental resource for early detection and screening. This study uses a specialized convolutional neural network to perform binary classification of chest X-ray images to classify drug-resistant and drug-sensitive TB. The models were trained and validated using the TBPortals dataset which contains 2,973 labeled X-ray images from TB patients. The classifiers were able to identify the presence or absence of drug-resistant Tuberculosis with an AUROC between 0.66–0.67, which is an improvement over previous attempts using deep learning networks.

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.

Automated detection of pneumonia cases using deep transfer learning with paediatric chest X-ray images

2021 ◽  
pp. 20201263
Author(s):  
Mohammad Salehi ◽  
Reza Mohammadi ◽  
Hamed Ghaffari ◽  
Nahid Sadighi ◽  
Reza Reiazi
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Area Under Curve ◽  
Binary Classification ◽  
Classification Performance ◽  
Learning Approach ◽  
Insufficient Data ◽  
X Ray ◽  
Chest X Ray

Objective: Pneumonia is a lung infection and causes the inflammation of the small air sacs (Alveoli) in one or both lungs. Proper and faster diagnosis of pneumonia at an early stage is imperative for optimal patient care. Currently, chest X-ray is considered as the best imaging modality for diagnosing pneumonia. However, the interpretation of chest X-ray images is challenging. To this end, we aimed to use an automated convolutional neural network-based transfer-learning approach to detect pneumonia in paediatric chest radiographs. Methods: Herein, an automated convolutional neural network-based transfer-learning approach using four different pre-trained models (i.e. VGG19, DenseNet121, Xception, and ResNet50) was applied to detect pneumonia in children (1–5 years) chest X-ray images. The performance of different proposed models for testing data set was evaluated using five performances metrics, including accuracy, sensitivity/recall, Precision, area under curve, and F1 score. Results: All proposed models provide accuracy greater than 83.0% for binary classification. The pre-trained DenseNet121 model provides the highest classification performance of automated pneumonia classification with 86.8% accuracy, followed by Xception model with an accuracy of 86.0%. The sensitivity of the proposed models was greater than 91.0%. The Xception and DenseNet121 models achieve the highest classification performance with F1-score greater than 89.0%. The plotted area under curve of receiver operating characteristics of VGG19, Xception, ResNet50, and DenseNet121 models are 0.78, 0.81, 0.81, and 0.86, respectively. Conclusion: Our data showed that the proposed models achieve a high accuracy for binary classification. Transfer learning was used to accelerate training of the proposed models and resolve the problem associated with insufficient data. We hope that these proposed models can help radiologists for a quick diagnosis of pneumonia at radiology departments. Moreover, our proposed models may be useful to detect other chest-related diseases such as novel Coronavirus 2019. Advances in knowledge: Herein, we used transfer learning as a machine learning approach to accelerate training of the proposed models and resolve the problem associated with insufficient data. Our proposed models achieved accuracy greater than 83.0% for binary classification.

scholarly journals Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection Using Chest X-ray

2020 ◽  
Vol 10 (9) ◽  
pp. 3233 ◽  
Author(s):  
Tawsifur Rahman ◽  
Muhammad E. H. Chowdhury ◽  
Amith Khandakar ◽  
Khandaker R. Islam ◽  
Khandaker F. Islam ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Viral Pneumonia ◽  
Deep Convolutional Neural Network ◽  
X Rays ◽  
X Ray ◽  
Life Threatening ◽  
Chest X Ray ◽  
The Right

Pneumonia is a life-threatening disease, which occurs in the lungs caused by either bacterial or viral infection. It can be life-endangering if not acted upon at the right time and thus the early diagnosis of pneumonia is vital. The paper aims to automatically detect bacterial and viral pneumonia using digital x-ray images. It provides a detailed report on advances in accurate detection of pneumonia and then presents the methodology adopted by the authors. Four different pre-trained deep Convolutional Neural Network (CNN): AlexNet, ResNet18, DenseNet201, and SqueezeNet were used for transfer learning. A total of 5247 chest X-ray images consisting of bacterial, viral, and normal chest x-rays images were preprocessed and trained for the transfer learning-based classification task. In this study, the authors have reported three schemes of classifications: normal vs. pneumonia, bacterial vs. viral pneumonia, and normal, bacterial, and viral pneumonia. The classification accuracy of normal and pneumonia images, bacterial and viral pneumonia images, and normal, bacterial, and viral pneumonia were 98%, 95%, and 93.3%, respectively. This is the highest accuracy, in any scheme, of the accuracies reported in the literature. Therefore, the proposed study can be useful in more quickly diagnosing pneumonia by the radiologist and can help in the fast airport screening of pneumonia patients.

scholarly journals Generalization Challenges in Drug-Resistant Tuberculosis Detection from Chest X-rays

Diagnostics ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 188
Author(s):  
Manohar Karki ◽  
Karthik Kantipudi ◽  
Feng Yang ◽  
Hang Yu ◽  
Yi Xiang J. Wang ◽  
...  
Keyword(s):  
Network Architecture ◽  
Cross Validation ◽  
Classification Model ◽  
Drug Resistant ◽  
X Rays ◽  
Drug Resistant Tuberculosis ◽  
X Ray ◽  
Resistant Tuberculosis ◽  
Unseen Data ◽  
Validation Set

Classification of drug-resistant tuberculosis (DR-TB) and drug-sensitive tuberculosis (DS-TB) from chest radiographs remains an open problem. Our previous cross validation performance on publicly available chest X-ray (CXR) data combined with image augmentation, the addition of synthetically generated and publicly available images achieved a performance of 85% AUC with a deep convolutional neural network (CNN). However, when we evaluated the CNN model trained to classify DR-TB and DS-TB on unseen data, significant performance degradation was observed (65% AUC). Hence, in this paper, we investigate the generalizability of our models on images from a held out country’s dataset. We explore the extent of the problem and the possible reasons behind the lack of good generalization. A comparison of radiologist-annotated lesion locations in the lung and the trained model’s localization of areas of interest, using GradCAM, did not show much overlap. Using the same network architecture, a multi-country classifier was able to identify the country of origin of the X-ray with high accuracy (86%), suggesting that image acquisition differences and the distribution of non-pathological and non-anatomical aspects of the images are affecting the generalization and localization of the drug resistance classification model as well. When CXR images were severely corrupted, the performance on the validation set was still better than 60% AUC. The model overfitted to the data from countries in the cross validation set but did not generalize to the held out country. Finally, we applied a multi-task based approach that uses prior TB lesions location information to guide the classifier network to focus its attention on improving the generalization performance on the held out set from another country to 68% AUC.

scholarly journals A Highly Reliable Convolutional Neural Network Based Soft Tissue Sarcoma Metastasis Detection from Chest X-ray Images: A Retrospective Cohort Study

Cancers ◽  
2021 ◽  
Vol 13 (19) ◽  
pp. 4961
Author(s):  
Christoph Wallner ◽  
Mansoor Alam ◽  
Marius Drysch ◽  
Johannes Maximilian Wagner ◽  
Alexander Sogorski ◽  
...  
Keyword(s):  
Neural Network ◽  
Soft Tissue ◽  
Convolutional Neural Network ◽  
Malignant Tumors ◽  
Soft Tissue Sarcomas ◽  
High Volume ◽  
X Rays ◽  
X Ray ◽  
Chest X Ray ◽  
Lung Ct

Introduction: soft tissue sarcomas are a subset of malignant tumors that are relatively rare and make up 1% of all malignant tumors in adulthood. Due to the rarity of these tumors, there are significant differences in quality in the diagnosis and treatment of these tumors. One paramount aspect is the diagnosis of hematogenous metastases in the lungs. Guidelines recommend routine lung imaging by means of X-rays. With the ever advancing AI-based diagnostic support, there has so far been no implementation for sarcomas. The aim of the study was to utilize AI to obtain analyzes regarding metastasis on lung X-rays in the most possible sensitive and specific manner in sarcoma patients. Methods: a Python script was created and trained using a set of lung X-rays with sarcoma metastases from a high-volume German-speaking sarcoma center. 26 patients with lung metastasis were included. For all patients chest X-ray with corresponding lung CT scans, and histological biopsies were available. The number of trainable images were expanded to 600. In order to evaluate the biological sensitivity and specificity, the script was tested on lung X-rays with a lung CT as control. Results: in this study we present a new type of convolutional neural network-based system with a precision of 71.2%, specificity of 90.5%, sensitivity of 94%, recall of 94% and accuracy of 91.2%. A good detection of even small findings was determined. Discussion: the created script establishes the option to check lung X-rays for metastases at a safe level, especially given this rare tumor entity.

scholarly journals FLANNEL  (Focal Loss bAsed Neural Network EnsembLe) for COVID-19 detection

2020 ◽  
Author(s):  
Zhi Qiao ◽  
Austin Bae ◽  
Lucas M Glass ◽  
Cao Xiao ◽  
Jimeng Sun
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Class Imbalance ◽  
X Rays ◽  
Neural Network Ensemble ◽  
Imaging Data ◽  
Identification Task ◽  
Neural Network Models ◽  
X Ray ◽  
Chest X Ray

Abstract Objective The study sought to test the possibility of differentiating chest x-ray images of coronavirus disease 2019 (COVID-19) against other pneumonia and healthy patients using deep neural networks. Materials and Methods We construct the radiography (x-ray) imaging data from 2 publicly available sources, which include 5508 chest x-ray images across 2874 patients with 4 classes: normal, bacterial pneumonia, non–COVID-19 viral pneumonia, and COVID-19. To identify COVID-19, we propose a FLANNEL (Focal Loss bAsed Neural Network EnsembLe) model, a flexible module to ensemble several convolutional neural network models and fuse with a focal loss for accurate COVID-19 detection on class imbalance data. Results FLANNEL consistently outperforms baseline models on COVID-19 identification task in all metrics. Compared with the best baseline, FLANNEL shows a higher macro-F1 score, with 6% relative increase on the COVID-19 identification task, in which it achieves precision of 0.7833 ± 0.07, recall of 0.8609 ± 0.03, and F1 score of 0.8168 ± 0.03. Discussion Ensemble learning that combines multiple independent basis classifiers can increase the robustness and accuracy. We propose a neural weighing module to learn the importance weight for each base model and combine them via weighted ensemble to get the final classification results. In order to handle the class imbalance challenge, we adapt focal loss to our multiple classification task as the loss function. Conclusion FLANNEL effectively combines state-of-the-art convolutional neural network classification models and tackles class imbalance with focal loss to achieve better performance on COVID-19 detection from x-rays.

scholarly journals Screening of chest X-Rays for Tuberculosis using Deep Convolutional Neural Network

2021 ◽  
Vol 9 (5) ◽  
pp. 254-258
Author(s):  
R. Rohith ◽  
S.P.Syed Ibrahim
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Medical Officer ◽  
Developing Nations ◽  
Deep Convolutional Neural Network ◽  
X Rays ◽  
X Ray ◽  
Life Threatening ◽  
Chest X Ray

Tuberculosis is a life-threatening disease that mainly affects underdeveloped as well as developing nations. While lethal it is often resistive to antibiotics and the safest way to treat a patient is to detect the disease's presence as soon as possible. Various techniques have been developed to diagnose tuberculosis and radiography of the chest is one of such methods that works well for over a decade.. Though an effective method still the success depends on the medical officer who examines the chest X-rays. Thus ,this paper proposes an approach for detecting X-ray abnormalities using deep learning. The systems output is assessed on two open Montgomery and Shenz en chest X-ray datasets and accuracy of 84 percent is achieved.

scholarly journals The diagnostic evaluation of Convolutional Neural Network (CNN) for the assessment of chest X-ray of patients infected with COVID-19

2020 ◽  
Author(s):  
Syed Usama Khalid Bukhari ◽  
Syed Safwan Khalid Bukhari ◽  
Asmara Syed ◽  
Syed Sajid Hussain Shah
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Health Professionals ◽  
Digital Images ◽  
Microbial Pathogens ◽  
X Rays ◽  
Data Set ◽  
X Ray ◽  
Radiological Abnormality ◽  
Chest X Ray

AbstractIntroductionThe main target of COVID-19 is the lungs where it may cause pneumonia in severely ill patients. Chest X-ray is an important diagnostic test to assess the lung for the damaging effects of COVID-19. Many other microbial pathogens can also cause damage to lungs leading to pneumonia but there are certain radiological features which can favor the diagnosis of pneumonia caused by COVID-19. With the rising number of cases of COVID-19, it would be imperative to develop computer programs which may assist the health professionals in the prevailing scenario.Materials & MethodsA total of two hundred and seventy eight (278) images of chest X-rays have been assessed by applying ResNet-50 convolutional neural network architectures in the present study. The digital images were acquired from the public repositories provided by University of Montreal and National Institutes of Health. These digital images of Chest X-rays were divided into three groups labeled as normal, pneumonia and COVID-19. The third group contains digital images of chest X-rays of patients diagnosed with COVID-19 infection while the second group contains images of lung with pneumonia caused by other pathogens.ResultsThe radiological images included in the data set are 89 images of lungs with COVID-19 infection, 93 images of lungs without any radiological abnormality and 96 images of patient with pneumonia caused by other pathogens. In this data set, 80% of the images were employed for training, and 20% for testing. A pre-trained (on ImageNet data set) ResNet-50 architecture was used to diagnose the cases of COVID-19 infections on lung X-ray images. The analysis of the data revealed that computer vision based program achieved diagnostic accuracy of 98.18 %, and F1-score of 98.19.ConclusionThe performance of convolutional neural network regarding the differentiation of pulmonary changes caused by COVID-19 from the other type of pneumonias on digital images of the chest X-rays is excellent and it may be an extremely useful adjunct tool for the health professionals.

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