scholarly journals Evaluation of diagnostic accuracy of the automatic system for the analysis of digital lung X-ray for detection of spherical masses

2020 ◽  
Vol 49 ◽  
Author(s):  
P. V. Gavrilov ◽  
U. A. Smolnikova
Keyword(s):  
Area Under The Curve ◽  
Diagnostic System ◽  
Automatic Identification ◽  
Diagnostic Efficiency ◽  
X Ray ◽  
Diagnostic Algorithms ◽  
Medical Institutions ◽  
Chest X Ray ◽  
State Registration ◽  
Abnormal Ratio

Rationale: Most data on the effectiveness of systems for the analysis of digital X-ray images have been provided by their developers and require a  high-quality validation in databases prepared independently of the developer.Aim: To analyze the information content of automatic identification of spherical lung masses with digital X-ray imaging using one of the widely available diagnostic algorithms on publicly unaccessible reference datasets.Materials and methods: The study was based on the recognition and analysis of digital X-ray images from two publicly inaccessible reference datasets that have the state registration (Russian Federation) with one of the publicly available diagnostic algorithms (FutureMed Analyzer). The study was performed using two models of X-ray screening as examples: Model  1 consisted of 100  X-ray images of the lungs with a  normal: abnormal ratio of 94%: 6%; Model  2 consisted of 5150 chest X-ray images with a normal: abnormal ratio of 97%: 3%.Results: According to the results of the analysis of the X-ray images with the diagnostic system, 98%  of the images were correctly interpreted with Model 1 and 95% of the images, with Model 2. 83% of the cases from Model  1 and 69% from Model  2% were interpreted as images with lung abnormalities. The percentage of correct answers for differentiation of the chest X-ray images into two categories (normal vs. abnormal) for Model 1 and Model  2 was 95% and 98%, respectively. The sensitivity for detection of abnormal masses ranged from 69% to 83%. The specificity was 99% for the Model 1 chest X-ray images and 96% for the Model  2 chest X-ray images. The underdiagnosis rate was quite low ranging for Model 1 – 17%, and for Model 2  – 31%. The area under the curve for Model 1 was 0.91 and for Model 2 0.85.Conclusion: The diagnostic efficiency of the automatic image analysis based on the convolutional neuronal networks approaches that of the radiologists. This system of automatic identification of abnormalities was unable to solve the most complex problems of detecting low density spherical masses (like "ground glass" area on computed tomography) and that of shadow summation for abnormalities located in such difficult to interpret zones as lung apices, clavicles, ribs, etc. To select a  suitable system, medical institutions need to conduct preliminary testing in their own models equivalent to the studies performed in a  given institution (parameters for radiography, nature and frequency of abnormalities).

scholarly journals Machine learning applied on chest x-ray can aid in the diagnosis of COVID-19: a first experience from Lombardy, Italy

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Isabella Castiglioni ◽  
Davide Ippolito ◽  
Matteo Interlenghi ◽  
Caterina Beatrice Monti ◽  
Christian Salvatore ◽  
...  
Keyword(s):  
Deep Learning ◽  
Area Under The Curve ◽  
Clinical Information ◽  
Training Dataset ◽  
Independent Dataset ◽  
X Ray ◽  
Learning Classifier ◽  
Chest X Ray ◽  
Polymerase Chain ◽  
Deep Learning Model

Abstract Background We aimed to train and test a deep learning classifier to support the diagnosis of coronavirus disease 2019 (COVID-19) using chest x-ray (CXR) on a cohort of subjects from two hospitals in Lombardy, Italy. Methods We used for training and validation an ensemble of ten convolutional neural networks (CNNs) with mainly bedside CXRs of 250 COVID-19 and 250 non-COVID-19 subjects from two hospitals (Centres 1 and 2). We then tested such system on bedside CXRs of an independent group of 110 patients (74 COVID-19, 36 non-COVID-19) from one of the two hospitals. A retrospective reading was performed by two radiologists in the absence of any clinical information, with the aim to differentiate COVID-19 from non-COVID-19 patients. Real-time polymerase chain reaction served as the reference standard. Results At 10-fold cross-validation, our deep learning model classified COVID-19 and non-COVID-19 patients with 0.78 sensitivity (95% confidence interval [CI] 0.74–0.81), 0.82 specificity (95% CI 0.78–0.85), and 0.89 area under the curve (AUC) (95% CI 0.86–0.91). For the independent dataset, deep learning showed 0.80 sensitivity (95% CI 0.72–0.86) (59/74), 0.81 specificity (29/36) (95% CI 0.73–0.87), and 0.81 AUC (95% CI 0.73–0.87). Radiologists’ reading obtained 0.63 sensitivity (95% CI 0.52–0.74) and 0.78 specificity (95% CI 0.61–0.90) in Centre 1 and 0.64 sensitivity (95% CI 0.52–0.74) and 0.86 specificity (95% CI 0.71–0.95) in Centre 2. Conclusions This preliminary experience based on ten CNNs trained on a limited training dataset shows an interesting potential of deep learning for COVID-19 diagnosis. Such tool is in training with new CXRs to further increase its performance.

scholarly journals Detection of COVID-19 from Chest X-Ray Images Using Convolutional Neural Networks

2020 ◽  
Vol 25 (6) ◽  
pp. 553-565 ◽  
Author(s):  
Boran Sekeroglu ◽  
Ilker Ozsahin
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Statistical Data ◽  
Area Under The Curve ◽  
Operating Characteristics ◽  
X Ray ◽  
Life Saving ◽  
Chest X Ray

The detection of severe acute respiratory syndrome coronavirus 2 (SARS CoV-2), which is responsible for coronavirus disease 2019 (COVID-19), using chest X-ray images has life-saving importance for both patients and doctors. In addition, in countries that are unable to purchase laboratory kits for testing, this becomes even more vital. In this study, we aimed to present the use of deep learning for the high-accuracy detection of COVID-19 using chest X-ray images. Publicly available X-ray images (1583 healthy, 4292 pneumonia, and 225 confirmed COVID-19) were used in the experiments, which involved the training of deep learning and machine learning classifiers. Thirty-eight experiments were performed using convolutional neural networks, 10 experiments were performed using five machine learning models, and 14 experiments were performed using the state-of-the-art pre-trained networks for transfer learning. Images and statistical data were considered separately in the experiments to evaluate the performances of models, and eightfold cross-validation was used. A mean sensitivity of 93.84%, mean specificity of 99.18%, mean accuracy of 98.50%, and mean receiver operating characteristics–area under the curve scores of 96.51% are achieved. A convolutional neural network without pre-processing and with minimized layers is capable of detecting COVID-19 in a limited number of, and in imbalanced, chest X-ray images.

Comparison of Baseline, Bone-Subtracted, and Enhanced Chest Radiographs for Detection of Pneumothorax

2020 ◽  
pp. 084653712090885
Author(s):  
Fatemeh Homayounieh ◽  
Subba R. Digumarthy ◽  
Jennifer A. Febbo ◽  
Sherief Garrana ◽  
Chayanin Nitiwarangkul ◽  
...  
Keyword(s):  
False Negative ◽  
Tension Pneumothorax ◽  
Area Under The Curve ◽  
Ground Truth ◽  
Chest Radiographs ◽  
Detection Rates ◽  
X Ray ◽  
Institutional Review ◽  
Type Area ◽  
Chest X Ray

Purpose: To assess and compare detectability of pneumothorax on unprocessed baseline, single-energy, bone-subtracted, and enhanced frontal chest radiographs (chest X-ray, CXR). Method and Materials: Our retrospective institutional review board–approved study included 202 patients (mean age 53 ± 24 years; 132 men, 70 women) who underwent frontal CXR and had trace, moderate, large, or tension pneumothorax. All patients (except those with tension pneumothorax) had concurrent chest computed tomography (CT). Two radiologists reviewed the CXR and chest CT for pneumothorax on baseline CXR (ground truth). All baseline CXR were processed to generate bone-subtracted and enhanced images (ClearRead X-ray). Four radiologists (R1-R4) assessed the baseline, bone-subtracted, and enhanced images and recorded the presence of pneumothorax (side, size, and confidence for detection) for each image type. Area under the curve (AUC) was calculated with receiver operating characteristic analyses to determine the accuracy of pneumothorax detection. Results: Bone-subtracted images (AUC: 0.89-0.97) had the lowest accuracy for detection of pneumothorax compared to the baseline (AUC: 0.94-0.97) and enhanced (AUC: 0.96-0.99) radiographs ( P < .01). Most false-positive and false-negative pneumothoraces were detected on the bone-subtracted images and the least numbers on the enhanced radiographs. Highest detection rates and confidence were noted for the enhanced images (empiric AUC for R1-R4 0.96-0.99). Conclusion: Enhanced CXRs are superior to bone-subtracted and unprocessed radiographs for detection of pneumothorax. Clinical Relevance/Application: Enhanced CXRs improve detection of pneumothorax over unprocessed images; bone-subtracted images must be cautiously reviewed to avoid false negatives.

scholarly journals Automated Detection of Multiple Lesions on Chest X-ray Images: Classification Using a Neural Network Technique with Association-Specific Contexts

2020 ◽  
Vol 10 (5) ◽  
pp. 1742 ◽  
Author(s):  
Shuaijing Xu ◽  
Junqi Guo ◽  
Guangzhi Zhang ◽  
Rongfang Bie
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Lung Cancer Mortality ◽  
Automated Detection ◽  
Classification Model ◽  
Diagnostic Efficiency ◽  
X Ray ◽  
Multiple Lesions ◽  
Model Combining ◽  
Chest X Ray

Automated detection of lung lesions on Chest X-ray images shows good performance to reduce lung cancer mortality. However, it is difficult to detect multiple lesions of single image well and truly, and additional efforts are needed to improve diagnostic efficiency and quality. In this paper, a multi-label classification model combining attention-based neural networks and association-specific contexts is proposed for the detection of multiple lesions on chest X-ray images. A convolutional neural network and a long short-term memory network are first aligned by an attention mechanism to take advantage of both image and text information for the detection, called CNN-ATTENTION-LSTM (CAL) network. In addition, a mining method of implicit association strength to obtain an association network of chest lesions (CLA) network is designed to guide the training of CAL network. The CLA network provides possible clinical relationships between lesions to help the CAL network obtain better predictions. Experimental results on ChestX-ray14 dataset show that our method outperforms some state-of-the-art models under the metrics of area under curve (AUC), precision, recall, and F-score and achieves up to 85.4% in the case of atelectasis and infiltration. It indicates that the method may be useful in the computer-aided detection of multiple lesions on chest X-ray images.

scholarly journals Artificial intelligence applied on chest X-ray can aid in the diagnosis of COVID-19 infection: a first experience from Lombardy, Italy

2020 ◽  
Author(s):  
Isabella Castiglioni ◽  
Davide Ippolito ◽  
Matteo Interlenghi ◽  
Caterina Beatrice Monti ◽  
Christian Salvatore ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Area Under The Curve ◽  
Training Dataset ◽  
List Type ◽  
X Rays ◽  
Independent Dataset ◽  
X Ray ◽  
Chest X Ray

AbstractObjectivesWe tested artificial intelligence (AI) to support the diagnosis of COVID-19 using chest X-ray (CXR). Diagnostic performance was computed for a system trained on CXRs of Italian subjects from two hospitals in Lombardy, Italy.MethodsWe used for training and internal testing an ensemble of ten convolutional neural networks (CNNs) with mainly bedside CXRs of 250 COVID-19 and 250 non-COVID-19 subjects from two hospitals. We then tested such system on bedside CXRs of an independent group of 110 patients (74 COVID-19, 36 non-COVID-19) from one of the two hospitals. A retrospective reading was performed by two radiologists in the absence of any clinical information, with the aim to differentiate COVID-19 from non-COVID-19 patients. Real-time polymerase chain reaction served as reference standard.ResultsAt 10-fold cross-validation, our AI model classified COVID-19 and non COVID-19 patients with 0.78 sensitivity (95% confidence interval [CI] 0.74–0.81), 0.82 specificity (95% CI 0.78–0.85) and 0.89 area under the curve (AUC) (95% CI 0.86–0.91). For the independent dataset, AI showed 0.80 sensitivity (95% CI 0.72–0.86) (59/74), 0.81 specificity (29/36) (95% CI 0.73–0.87), and 0.81 AUC (95% CI 0.73– 0.87). Radiologists’ reading obtained 0.63 sensitivity (95% CI 0.52–0.74) and 0.78 specificity (95% CI 0.61–0.90) in one centre and 0.64 sensitivity (95% CI 0.52–0.74) and 0.86 specificity (95% CI 0.71–0.95) in the other.ConclusionsThis preliminary experience based on ten CNNs trained on a limited training dataset shows an interesting potential of AI for COVID-19 diagnosis. Such tool is in training with new CXRs to further increase its performance.Key pointsArtificial intelligence based on convolutional neural networks was preliminary applied to chest-X-rays of patients suspected to be infected by COVID-19.Convolutional neural networks trained on a limited dataset of 250 COVID-19 and 250 non-COVID-19 were tested on an independent dataset of 110 patients suspected for COVID-19 infection and provided a balanced performance with 0.80 sensitivity and 0.81 specificity.Training on larger multi-institutional datasets may allow this tool to increase its performance.

scholarly journals Chest X-ray scanning based detection of COVID-19 using deepconvolutional neural network

2020 ◽  
Author(s):  
Samir Yadav ◽  
Jasminder Kaur Sandhu ◽  
Yadunath Pathak ◽  
Shivajirao Jadhav
Keyword(s):  
Neural Network ◽  
Low Cost ◽  
Diagnostic System ◽  
Multiclass Classification ◽  
Expert Advice ◽  
X Rays ◽  
X Ray ◽  
Chest X Ray ◽  
Using Data ◽  
Life On Earth

Abstract Everyone’s life on earth influenced by a global coronavirus outbreak COVID- 19. Two regular practices, pathology tests, and Computer Tomography (CT) scan used to diagnose COVID-19. Pathology tests produce a considerable amount of false-positives & are time-consuming, whereas CT scans tests are costly and require expert advice. Hence, the main aim of this work is to develop a fast, accurate, and low-cost diagnostic system for detection of COVID-19 using inexpensive chest X-rays and the modern Deep Convolutional Neural Network(CNN) approach to assist medical professionals. In this study, two pre-trained CNN models (VGG16 and InceptionV3) are evaluated by several experiments using data augmentations. The analysis is based on 2905 images of chest X-rays with 219 confirmed positive COVID-19 and 1345 positive pneumonia cases taken from the open-source database consisting of patients suffering from the COVID-19 disease. Since a database consists of multiple types of diseases, multiclass classification for diagnosis of COVID-19 is used. The InceptionV3 model provides the highest classification accuracy (99.35% and 98.29%) for two binary classifications (normal vs. COVID-19 and COVID- 19 vs. Pneumonia) compare to VGG16 model’s accuracy (97.71% and 96.27%). Whereas, VGG16 provides highest accuracy (98.84%)for multiclass-classification(normal vs COVID- 19 vs pneumonia) as compared to VGG16 model’s accuracy(96.35%).

scholarly journals Simple method for detecting idiopathic interstitial pneumonias by measuring vertical lung length on chest X-ray

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Masato Karayama ◽  
Yoichiro Aoshima ◽  
Hideki Yasui ◽  
Hironao Hozumi ◽  
Yuzo Suzuki ◽  
...  
Keyword(s):  
Operating Characteristic ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
X Rays ◽  
Simple Method ◽  
X Ray ◽  
Idiopathic Interstitial Pneumonias ◽  
Chest X Ray ◽  
Costophrenic Angle ◽  
Tracheal Carina

AbstractDetection of idiopathic interstitial pneumonias (IIPs) on chest X-ray is difficult for non-specialist physicians, especially in patients with mild IIPs. The current study aimed to evaluate the usefulness of a simple method for detecting IIPs by measuring vertical lung length (VLL) in chest X-rays to quantify decreased lung volume. A total of 280 consecutive patients with IIPs were randomly allocated to exploratory and validation cohorts, and 140 controls were selected for each cohort by propensity score-matching. Upper (uVLL; from apex to tracheal carina), lower (lVLL; from carina to costophrenic angle), and total VLL (tVLL; from apex to costophrenic angle), and the l/uVLL ratio were measured on chest X-rays. Patients in the exploratory cohort had significantly decreased uVLL, lVLL, tVLL, and l/uVLL ratio compared with controls (all p < 0.001). Receiver operating characteristic curve analyses demonstrated that lVLL (area under the curve [AUC] 0.86, sensitivity 0.65, specificity 0.92), tVLL (AUC 0.83, sensitivity 0.75, specificity 0.80), and l/uVLL ratio (AUC 0.80, sensitivity 0.72, specificity 0.79) had high diagnostic accuracies for IIPs. These results were reproduced in the validation cohort. IIP patients thus have decreased VLLs, and measurements of VLL may thus aid the accurate detection of IIPs.

scholarly journals Attention-Based Transfer Learning for Efficient Pneumonia Detection in Chest X-ray Images

2021 ◽  
Vol 11 (3) ◽  
pp. 1242
Author(s):  
So-Mi Cha ◽  
Seung-Seok Lee ◽  
Bonggyun Ko
Keyword(s):  
Transfer Learning ◽  
Area Under The Curve ◽  
Attention Mechanism ◽  
Stable Model ◽  
X Rays ◽  
X Ray ◽  
Learning Framework ◽  
Computer Aided ◽  
Common Technique ◽  
Chest X Ray

Pneumonia is a form of acute respiratory infection commonly caused by germs, viruses, and fungi, and can prove fatal at any age. Chest X-rays is the most common technique for diagnosing pneumonia. There have been several attempts to apply transfer learning based on a Convolutional Neural Network to build a stable model in computer-aided diagnosis. Recently, with the appearance of an attention mechanism that automatically focuses on the critical part of the image that is crucial for the diagnosis of disease, it is possible to increase the performance of previous models. The goal of this study is to improve the accuracy of a computer-aided diagnostic approach that medical professionals can easily use as an auxiliary tool. In this paper, we proposed the attention-based transfer learning framework for efficient pneumonia detection in chest X-ray images. We collected features from three-types of pre-trained models, ResNet152, DenseNet121, ResNet18 as a role of feature extractor. We redefined the classifier for a new task and applied the attention mechanism as a feature selector. As a result, the proposed approach achieved accuracy, F-score, Area Under the Curve(AUC), precision and recall of 96.63%, 0.973, 96.03%, 96.23% and 98.46%, respectively.

scholarly journals A New Optimal Diagnosis System for Coronavirus (COVID-19) Diagnosis Based on Archimedes Optimization Algorithm on Chest X-Ray Images

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Liping Chen ◽  
Tahereh Rezaei
Keyword(s):  
Respiratory Distress ◽  
Optimization Algorithm ◽  
State Of The Art ◽  
Diagnostic System ◽  
Histogram Equalization ◽  
X Ray ◽  
The World ◽  
Chest X Ray ◽  
Normal Scale

The new coronavirus, COVID-19, has affected people all over the world. Coronaviruses are a large group of viruses that can infect animals and humans and cause respiratory distress; these discomforts may be as mild as a cold or as severe as pneumonia. Correct detection of this disease can help to avoid its spreading increasingly. In this paper, a new CAD-based approach is suggested for the optimal diagnosis of this disease from chest X-ray images. The proposed method starts with a min-max normalization to scale all data into a normal scale, and then, histogram equalization is performed to improve the quality of the image before main processing. Afterward, 18 different features are extracted from the image. To decrease the method difficulty, the minimum features are selected based on a metaheuristic called Archimedes optimization algorithm (AOA). The model is then implemented on three datasets, and its results are compared with four other state-of-the-art methods. The final results indicated that the proposed method with 86% accuracy and 96% precision has the highest balance between accuracy and reliability with the compared methods as a diagnostic system for COVID-19.

scholarly journals EpistoNet: An ensemble of Epistocracy-optimized mixture of experts for detecting COVID-19 on chest X-ray images

2021 ◽  
Author(s):  
Seyed Ziae Mousavi Mojab ◽  
Seyedmohammad Shams ◽  
Farshad Fotouhi ◽  
Hamid Soltanian-Zadeh
Keyword(s):  
Neural Networks ◽  
Clustering Algorithm ◽  
Diagnostic System ◽  
Ensemble Model ◽  
Deep Convolutional Neural Networks ◽  
X Ray ◽  
The Public ◽  
Chest X Ray ◽  
Gating Network ◽  
The Impact

Abstract The Coronavirus has spread across the world and infected millions of people, causing devastating damage to the public health and global economies. To mitigate the impact of the coronavirus a reliable, fast, and accurate diagnostic system should be promptly implemented. In this study, we propose EpistoNet, a decision tree-based ensemble model using two mixtures of discriminative experts to classify COVID-19 lung infection from chest X-ray images. To optimize the architecture and hyper-parameters of the designed neural networks, we employed Epistocracy algorithm, a novel hyper-heuristic evolutionary method. Using 2,500 chest X-ray images consisting of 1,250 COVID-19 and 1,250 non-COVID-19 cases, we left out 500 images for testing and partitioned the remaining 2,000 images into 5 different clusters using K-means clustering algorithm. We trained multiple deep convolutional neural networks on each cluster to help build a mixture of strong discriminative experts from the top-performing models supervised by a gating network. The final ensemble model obtained 95% accuracy on COVID-19 images and 93% accuracy on non-COVID-19. The experimental results show that EpistoNet can accurately, and reliably be used to detect COVID-19 infection in the chest X-ray images, and Epistocracy algorithm can be effectively used to optimize the hyper-parameters of the proposed models.

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