Query: Automatic diagnosis of pneumonia from Chest x Ray Images

title: An optimized transfer learning based approach for automatic diagnosis of COVID 19 from chest x ray images
abstract: Accurate and fast detection of COVID 19 patients is crucial to control this pandemic. Due to the scarcity of COVID 19 testing kits, especially in developing countries, there is a crucial need to rely on alternative diagnosis methods. Deep learning architectures built on image modalities can speed up the COVID 19 pneumonia classification from other types of pneumonia. The transfer learning approach is better suited to automatically detect COVID 19 cases due to the limited availability of medical images. This paper introduces an Optimized Transfer Learning based Approach for Automatic Detection of COVID 19 (OTLD COVID 19) that applies an optimization algorithm to twelve CNN architectures to diagnose COVID 19 cases using chest x ray images. The OTLD COVID 19 approach adapts Manta Ray Foraging Optimization (MRFO) algorithm to optimize the network hyperparameters' values of the CNN architectures to improve their classification performance. The proposed dataset is collected from eight different public datasets to classify 4 class cases (COVID 19, pneumonia bacterial, pneumonia viral, and normal) The experimental result showed that DenseNet121 optimized architecture achieves the best performance. The evaluation results based on Loss, Accuracy, F1 score, Precision, Recall, Specificity, AUC, Sensitivity, IoU, and Dice values reached 0.0523, 98.47% 0.9849, 98.50% 98.47% 99.50% 0.9983, 0.9847, 0.9860, and 0.9879 respectively.

title: DeepCOVIDExplainer: Explainable COVID 19 Diagnosis Based on Chest X ray Images
abstract: Amid the coronavirus disease(COVID 19) pandemic, humanity experiences a rapid increase in infection numbers across the world. Challenge hospitals are faced with, in the fight against the virus, is the effective screening of incoming patients. One methodology is the assessment of chest radiography(CXR) images, which usually requires expert radiologist's knowledge. In this paper, we propose an explainable deep neural networks(DNN) based method for automatic detection of COVID 19 symptoms from CXR images, which we call DeepCOVIDExplainer. We used 15,959 CXR images of 15,854 patients, covering normal, pneumonia, and COVID 19 cases. CXR images are first comprehensively preprocessed, before being augmented and classified with a neural ensemble method, followed by highlighting class discriminating regions using gradient guided class activation maps(Grad CAM+ and layer wise relevance propagation(LRP) Further, we provide human interpretable explanations of the predictions. Evaluation results based on hold out data show that our approach can identify COVID 19 confidently with a positive predictive value(PPV) of 91.6% 92.45% and 96.12% precision, recall, and F1 score of 94.6% 94.3% and 94.6% respectively for normal, pneumonia, and COVID 19 cases, respectively, making it comparable or improved results over recent approaches. We hope that our findings will be a useful contribution to the fight against COVID 19 and, in more general, towards an increasing acceptance and adoption of AI assisted applications in the clinical practice.

title: Predict pneumonia with chest X ray images based on convolutional deep neural learning networks
abstract: The chest X ray examination is one of the most important methods for screening and diagnosing of many lung diseases. Diagnosis of pneumonia by chest X ray is one of the common methods used by medical experts. However, the image quality of chest X Ray has some defects, such as low contrast, overlapping organs and blurred boundary, which seriously affects detecting pneumonia in chest X rays. Therefore, it has important medical value and application significance to construct a stable and accurate automatic detection model of pneumonia through a large number of chest X ray images. In this paper, we propose a novel hybrid system for detecting pneumonia from chest X Ray image: ACNN RF, which is an adaptive median filter Convolutional Neural Network (CNN) recognition model based on Random forest (RF) Firstly, the improved adaptive median filtering is employed to remove noise in the chest X ray image, which makes the image more easily recognized. Secondly, we establish the CNN architecture based on Dropout to extract deep activation features from each chest X ray image. Finally, we employ the RF classifier based on GridSearchCV class as a classifier for deep activation features in CNN model. It not only avoids the phenomenon of over fitting in data training, but also improves the accuracy of image classification. During our experiment, the public chest X ray image dataset used in the experiment contains 5863 images, which comprises 4265 frontal view X ray images of 1574 unique patients. The average recognition rate of pneumonia is up to 97% by the proposed ACNN RF. The experimental results show that the ACNN RF identification system is more effective than the previous traditional image identification system.

title: COVID 19 Detection from Chest X Ray Images Using CNNs Models: Further Evidence from Deep Transfer Learning
abstract: In this study we revisit the identification of COVID 19 from chest x ray images using Deep Learning. We collect a relatively large COVID 19 dataset comparing with previous studies that contains 309 real COVID 19 chest x ray images. We prepare also 2000 chest x ray images of pneumonia cases and 1000 images of healthy chest cases. Deep Transfer Learning is used to detect abnormalities in our images dataset. We fine tune three pre trained deep convolutional neural networks (CNNs) models on a training dataset; DenseNet 121, NASNetLarge and NASNetMobile. The evaluation of our models on a test dataset shows that these models achieve a sensitivity rate of around 99.45 on average, and a specificity rate of around 99.5 on average. These results could be helpful for an automatic diagnosis of COVID 19 infections, but the clinical diagnosis stills always necessary.

title: SARS n CoV2 19 detection from chest x ray images using deep neural networks
abstract: Novel coronavirus is fast spreading pathogen worldwide and is threatening billions of lives. SARS n CoV2 is known to affect the lungs of the COVID 19 positive patients. Chest x rays are the most widely used imaging technique for clinical diagnosis due to fast imaging time and low cost. The purpose of this study is to use deep learning technique for automatic detection of COVID 19 using chest x rays.  The authors used a data set containing confirmed COVID 19 positive, common bacterial pneumonia and healthy cases (no infection) A collection of 1,428 x ray images is used in this study. The authors used a pre trained VGG 16 model for the classification task. Transfer learning with fine tuning was used in this study to effectively train the network on a relatively small chest x ray data set. Initial experiments show that the model achieves promising results and can be greatly used to expedite COVID 19 detection.  The authors achieved an accuracy of 96% and 92.5% in two and three output class cases, respectively. Based on these findings, the medical community can access using x ray images as possible diagnostic tool for faster COVID 19 detection to complement the already testing and diagnosis methods.  The proposed method can be used as initial screening which can help health care professionals to better treat the COVID patients by timely detecting and screening the presence of disease.

title: DeepCOVIDExplainer: Explainable COVID 19 Diagnosis from Chest X ray Images
abstract: In this paper1, we proposed an explainable deep neural networks (DNN) based method for automatic detection of COVID 19 symptoms from chest radiography (CXR) images, which we call 'DeepCOVIDExplainer' We used 15,959 CXR images of 15,854 patients, covering normal, pneumonia, and COVID 19 cases. CXR images are first comprehensively preprocessed and augmented before classifying with a neural ensemble method, followed by highlighting class discriminating regions using gradient guided class activation maps (Grad CAM and layer wise relevance propagation (LRP) Further, we provide human interpretable explanations for the diagnosis. Evaluation results show that our approach can identify COVID 19 cases with a positive predictive value (PPV) of 91.6% 92.45% and 96.12% respectively for normal, pneumonia, and COVID 19 cases, respectively, outperforming recent approaches.1Read longer version of this paper: https:/arxiv.org/pdf/2004.04582.pdf

title: Understanding Automatic Pneumonia Classification Using Chest X Ray Images
abstract: Pneumonia has been recognized as a common and potentially lethal condition for nearly two centuries. The COVID 19 disease caused by the SARS CoV 2 virus first appeared in Wuhan, China, and is considered a serious disease due to its high permeability, and contagiousness. Patients with COVID 19 may suffer from cough, fever, tiredness, dyspnea, and other signs and symptoms similar to those of tuberculosis (TB) and other respiratory infections disease. The similarity of COVID 19 disease with other lung infections, along with its high spreading rate, makes the diagnosis difficult. Solutions based on machine learning techniques achieved relevant results in identifying the correct disease and providing early diagnosis, and can hence provide significant clinical decision support;however, such approaches suffer from the lack of proper means for interpreting the choices made by the models, especially in case of deep learning ones. With the aim to improve interpretability and explainability in the process of making qualified decisions, we designed a system that allows a partial opening of this black box by means of proper investigations on the rationale behind the decisions. We tested our approach over artificial neural networks trained for multiple classifications based on Chest X ray images;our tool analyzed the internal processes performed by the networks during the classification tasks to identify the most important elements involved in the training process that influence the network's decisions. We report the results of an experimental analysis aimed at assessing the viability of the proposed approach.

title: Diagnosis of Pediatric Pneumonia with Ensemble of Deep Convolutional Neural Networks in Chest X Ray Images
abstract: Pneumonia is a fatal disease that appears in the lungs and is caused by viral or bacterial infection. Diagnosis of pneumonia in chest X ray images can be difficult and error prone because of its similarity with other infections in the lungs. The aim of this study is to develop a computer aided pneumonia detection system to facilitate the diagnosis decision process. Therefore, a convolutional neural network (CNN) ensemble method was proposed for the automatic diagnosis of pneumonia which is seen in children. In this context, seven well known CNN models (VGG 16, VGG 19, ResNet 50, Inception V3, Xception, MobileNet, and SqueezeNet) pre trained on the ImageNet dataset were trained with the appropriate transfer learning and fine tuning strategies on the chest X ray dataset. Among the seven different models, the three most successful ones were selected for the ensemble method. The final results were obtained by combining the predictions of CNN models with the ensemble method during the test. In addition, a CNN model was trained from scratch, and the results of this model were compared with the proposed ensemble method. The proposed ensemble method achieved remarkable results with an AUC of 95.21 and a sensitivity of 97.76 on the test data. Also, the proposed ensemble method achieved classification accuracy of 90.71 in chest X ray images as normal, viral pneumonia, and bacterial pneumonia.

title: Automatic Detection of Pneumonia in Chest X Ray Images Using Cooperative Convolutional Neural Networks
abstract: Chest X ray images are currently the best available visual media for diagnosing pneumonia, which plays a crucial role in clinical care. Medical images diagnosing can be error prone for inexperienced radiologists, while tedious and time consuming for experienced radiologists. To address these issues, we study automatically detect pneumonia in Chest X ray images. However, this task exists several challenges. First, abnormal regions of pneumonia are difficult to identify due to the noise interference from other tissue and lesions. Second, the features of lung regions are usually essential information for diagnosis. With the pneumonia disease happens in lung areas, only training CNNs using global image may be affected by the irrelevant noisy regions. Third, the appearance of pneumonia in X ray images is often vague, can overlap with other diagnoses. To cope with these challenges, we first introduce a lung segmentation network, which segments the lung from the original images. Then, develop a feature extraction model, which incorporates global and local features for pneumonia classification. Finally, build a cooperative learning framework, which merges bounding boxes (localization of pneumonia) from two cooperative models. We demonstrate the effectiveness of our proposed methods on the Kaggles RSNA Pneumonia Detection Challenge dataset and reaches excellent performance with the level of the top 1% in this competition.

title: Automatic classification between COVID 19 pneumonia, non COVID 19 pneumonia, and the healthy on chest X ray image: combination of data augmentation methods
abstract: This study aimed to develop and validate computer aided diagnosis (CXDx) system for classification between COVID 19 pneumonia, non COVID 19 pneumonia, and the healthy on chest X ray (CXR) images. From two public datasets, 1248 CXR images were obtained, which included 215, 533, and 500 CXR images of COVID 19 pneumonia patients, non COVID 19 pneumonia patients, and the healthy samples, respectively. The proposed CADx system utilized VGG16 as a pre trained model and combination of conventional method and mixup as data augmentation methods. Other types of pre trained models were compared with the VGG16 based model. Single type or no data augmentation methods were also evaluated. Splitting of training/validation/test sets was used when building and evaluating the CADx system. Three category accuracy was evaluated for test set with 125 CXR images. The three category accuracy of the CAD system was 83.6% between COVID 19 pneumonia, non COVID 19 pneumonia, and the healthy. Sensitivity for COVID 19 pneumonia was more than 90% The combination of conventional method and mixup was more useful than single type or no data augmentation method. In conclusion, this study was able to create an accurate CADx system for the 3 category classification. Source code of our CADx system is available as open source for COVID 19 research.