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

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 in the right time and thus an early diagnosis of pneumonia is vital. The aim of this paper is to automatically detect bacterial and viral pneumonia using digital x-ray images. It provides a detailed report on advances made in making 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. 5247 Bacterial, viral and normal chest x-rays images underwent preprocessing techniques and the modified images were trained for the transfer learning based classification task. In this work, 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 than the accuracies reported in the literature. Therefore, the proposed study can be useful in faster-diagnosing pneumonia by the radiologist and can help in the fast airport screening of pneumonia patients.Comment: 13 Figures, 5 tables. arXiv admin note: text overlap with arXiv:2003.1314

Two-stage deep learning architecture for pneumonia detection and its diagnosis in chest radiographs

Approximately two million pediatric deaths occur every year due to Pneumonia. Detection and diagnosis of Pneumonia plays an important role in reducing these deaths. Chest radiography is one of the most commonly used modalities to detect pneumonia. In this paper, we propose a novel two-stage deep learning architecture to detect pneumonia and classify its type in chest radiographs. This architecture contains one network to classify images as either normal or pneumonic, and another deep learning network to classify the type as either bacterial or viral. In this paper, we study and compare the performance of various stage one networks such as AlexNet, ResNet, VGG16 and Inception-v3 for detection of pneumonia. For these networks, we employ transfer learning to exploit the wealth of information available from prior training. For the second stage, we find that transfer learning with these same networks tends to overfit the data. For this reason we propose a simpler CNN architecture for classification of pneumonic chest radiographs and show that it overcomes the overfitting problem. We further enhance the performance of our system in a novel way by incorporating lung segmentation using a U-Net architecture. We make use of a publicly available dataset comprising 5856 images (1583 - Normal, 4273 - Pneumonic). Among the pneumonia patients, 2780 patients are identified as bacteria type and the rest belongs to virus category. We test our proposed algorithm(s) on a set of 624 images and we achieve an area under the receiver operating characteristic curve of 0.996 for pneumonia detection. We also achieve an accuracy of 97.8% for classification of pneumonic chest radiographs thereby setting a new benchmark for both detection and diagnosis. We believe the proposed two-stage classification of chest radiographs for pneumonia detection and its diagnosis would enhance the workflow of radiologists

Can AI help in screening Viral and COVID-19 pneumonia?

Coronavirus disease (COVID-19) is a pandemic disease, which has already caused thousands of causalities and infected several millions of people worldwide. Any technological tool enabling rapid screening of the COVID-19 infection with high accuracy can be crucially helpful to healthcare professionals. The main clinical tool currently in use for the diagnosis of COVID-19 is the Reverse transcription polymerase chain reaction (RT-PCR), which is expensive, less-sensitive and requires specialized medical personnel. X-ray imaging is an easily accessible tool that can be an excellent alternative in the COVID-19 diagnosis. This research was taken to investigate the utility of artificial intelligence (AI) in the rapid and accurate detection of COVID-19 from chest X-ray images. The aim of this paper is to propose a robust technique for automatic detection of COVID-19 pneumonia from digital chest X-ray images applying pre-trained deep-learning algorithms while maximizing the detection accuracy. A public database was created by the authors combining several public databases and also by collecting images from recently published articles. The database contains a mixture of 423 COVID-19, 1485 viral pneumonia, and 1579 normal chest X-ray images. Transfer learning technique was used with the help of image augmentation to train and validate several pre-trained deep Convolutional Neural Networks (CNNs). The networks were trained to classify two different schemes: i) normal and COVID-19 pneumonia; ii) normal, viral and COVID-19 pneumonia with and without image augmentation. The classification accuracy, precision, sensitivity, and specificity for both the schemes were 99.7%, 99.7%, 99.7% and 99.55% and 97.9%, 97.95%, 97.9%, and 98.8%, respectively.Comment: 12 pages, 9 Figure

A hybrid deep learning approach towards building an intelligent system for pneumonia detection in chest X-ray images

Pneumonia is a major cause for the death of children. In order to overcome the subjectivity and time consumption of the traditional detection of pneumonia from chest X-ray images; this work hypothesized that a hybrid deep learning system that consists of a convolutional neural network (CNN) model with another type of classifiers will improve the performance of the detection system. Three types of classifiers (support vector machine (SVM), k-nearest neighbor (KNN), and random forest (RF) were used along with the traditional CNN classification system (Softmax) to automatically detect pneumonia from chest X-ray images. The performance of the hybrid systems was comparable to that of the traditional CNN model with Softmax in terms of accuracy, precision, and specificity; except for the RF hybrid system which had less performance than the others. On the other hand, KNN hybrid system had the best consumption time, followed by the SVM, Softmax, and lastly the RF system. However, this improvement in consumption time (up to 4 folds) was in the expense of the sensitivity. A new hybrid artificial intelligence methodology for pneumonia detection has been implemented using small-sized chest X-ray images. The novel system achieved a very efficient performance with a short classification consumption time

Feature selection using regression mutual information deep convolution neuron networks for COVID-19 X-ray image classification

Chest radiography (CXR) image is usually required for lung severity assessment. However, chest X-rays in COVID-19 interpretation is required expert radiologists’ knowledge. This study aims to improve the COVID-19 X-ray image classification using feature selection technique by the regression mutual information deep convolution neuron networks (RMI Deep-CNNs). The dataset consists of 219 COVID-19, 500 viral pneumonias, and 500 normal chest X-ray images. CXR images were comprehensively pre-trained using DCNNs to extract the very large image features, then, the feature selection could reduce the complexity of a model and reduce the model overfitting. Therefore, the critical features were selected using regression mutual information followed by the fully connected with softmax layer for classification. For the classification of two alternative systems, these networks were compared (ResNet152V2 and InceptionV3). The classification performance for both schemes were 92.21%, 100%, 90% and 91.39%, 100%, 82.50%, respectively. In addition, RMI Deep-CNNs not only improve the accuracy but also reduce trainable features by over 80%. This approach tends to significantly improve the computation time and model accuracy for COVID‐19 classification

Classification of pneumonia from X-ray images using siamese convolutional network

Pneumonia is one of the highest global causes of deaths especially for children under 5 years old. This happened mainly because of the difficulties in identifying the cause of pneumonia. As a result, the treatment given may not be suitable for each pneumonia case. Recent studies have used deep learning approaches to obtain better classification within the cause of pneumonia. In this research, we used siamese convolutional network (SCN) to classify chest x-ray pneumonia image into 3 classes, namely normal conditions, bacterial pneumonia, and viral pneumonia. Siamese convolutional network is a neural network architecture that learns similarity knowledge between pairs of image inputs based on the differences between its features. One of the important benefits of classifying data with SCN is the availability of comparable images that can be used as a reference when determining class. Using SCN, our best model achieved 80.03% accuracy, 79.59% f1 score, and an improved result reasoning by providing the comparable images

A Meta-Analysis of Evolution of Deep Learning Research in Medical Image Analysis

With a text mining and bibliometrics approach, we review the literature on the evolution of deep learning in medical image literature from 2012 – 2020 in order to understand the current state of the research and to identify the major research themes in image analysis to answer our research questions: RQ1: What are the learning modes that are evident in the literature? RQ2: What are the emerging learning modes in the literature? RQ3: What are the major themes in medical imaging literature? The analysis of 8704 resulting from a data collection process from peer-reviewed databases, our analysis discovered the six major themes of image segmentation studies, studies with image classification, evaluation procedures such as sensitivity and specificity, optical coherence tomography studies, MRI imaging studies, and Chest imaging studies. Additionally, we assessed the number of articles published each year, the frequent keywords, the author networks, the trending topics, and connections to other topics. We discovered that segmenting and classifying the images are the most common tasks. Transfer learning is the most researched area and cancer is the highly targeted disease and Covid-19 is the most recent research tren

Characterization of pneumonia among children under five years of age hospitalized in Thimphu, Bhutan

[eng] The general objective of this thesis was to describe the epidemiology, aetiology, clinical presentation, and radiological findings of pneumonia among Bhutanese children to better characterize childhood pneumonia in Bhutan and to contribute to the understanding of this disease in the local context. This thesis also aimed to assess the diagnostic and prognostic performance of host-response biomarkers alone, combined, or in addition to clinical scoring scales to risk-stratify children hospitalized with pneumonia and predict their outcome. The first article acknowledges the need for local research in Bhutan and comments on the specific challenges experienced when trying to conduct it. The second article is a systematic review that summarizes current knowledge around childhood pneumonia in Bhutan and identifies knowledge gaps in this area. The findings of this review were used as the starting point to guide further research and to establish the objectives of the Respiratory Infections in Bhutanese Children (RIBhuC) study. We reported the findings of the RIBhuC study in articles 3 to 6 of this thesis. In brief, the RIBhuC study took place between 1 July 2017 and 30 June 2018. We prospectively enrolled all children between 2 and 59 months admitted to the Jigme Dorji Wangchuck National Referral Hospital (JDWNRH) in Thimphu with WHO-defined clinical pneumonia, provided parents or caregivers consented to study participation. On admission, we performed a comprehensive physical examination, including anthropometric and vital signs measurements. We recorded demographic and clinical data from medical files and through family interviews. We performed an antero-posterior chest radiograph within 24 hours of admission and classified children according to radiological findings following WHO radiological criteria. We collected blood samples upon enrolment or as soon as possible after enrolment for haematology, biochemistry, and bacterial culture, and two drops of blood on filter paper for the identification of Streptococcus pneumoniae by realtime polymerase chain reaction (RT-PCR). In addition, we measured plasma levels of eleven host-response biomarkers, including six markers of immune and endothelial activation: interleukin-6 (IL-6), interleukin-8 (IL-8), soluble triggering receptor expressed on myeloid cells-1 (sTREM-1), soluble tumour necrosis factor receptor 1 (sTNFR1), angiopoietin-2 (Angpt-2), and soluble fms-like tyrosine kinase 1 (sFlt1). Finally, we collected respiratory samples through nasopharyngeal washing for the molecular identification of seventeen respiratory viruses and four atypical bacteria and the detection and capsular typing of Streptococcus pneumoniae. The third article describes the aetiological profile and the demographic and clinical characteristics of this cohort of children admitted with WHO-defined clinical pneumonia. The fourth article reports data on the prevalence of pneumococcal nasopharyngeal carriers and on the pneumococcal serotypes circulating among Bhutanese children with clinical pneumonia before the introduction of the pneumococcal conjugate vaccine in the country. We identified and compared respiratory viruses among children with and without pneumococcal nasopharyngeal colonization to contribute to the understanding of the interplay between pneumococcal nasopharyngeal colonization and viral coinfections. The fifth article describes the radiological findings of the RIBhuC cohort and the differences in radiological outcomes by demographic characteristics, aetiology, clinical features, and host-response biomarker levels. We also evaluated the utility of hostresponse biomarkers in discerning between bacterial and viral pneumonia, taking radiological endpoint pneumonia as a proxy for bacterial aetiology. The sixth and last article of this thesis assessed the performance of a wide range of clinical characteristics, laboratory testing, clinical scoring scales, and host-response biomarkers to risk-stratify children with clinical pneumonia in Bhutan and predict their outcome.[spa] El objetivo principal de esta tesis fue identificar y reducir las lagunas de conocimiento sobre la epidemiologia, la etiologia, la presentacion clinica y los hallazgos radiologicos de la neumonia infantil en Butan para caracterizar esta enfermedad y contribuir a su comprension en el contexto local. Esta tesis tambien tuvo como objetivo evaluar el rol diagnostico y pronostico de ciertos biomarcadores por si solos, combinados o en adicion a escalas de puntuacion clinica para estratificar el riesgo de los ninos hospitalizados con neumonia y predecir su resultado clinico. El primer artículo senala la necesidad de realizar investigacion a nivel local y comenta los desafios especificos encontrados en un pais como Butan para llevarla a cabo. El segundo artículo es una revision sistematica que resume el conocimiento actual sobre la neumonia infantil en Butan y que identifica las lagunas de conocimiento en este campo. Se utilizaron los resultados de esta revision y las carencias de conocimiento para enfocar los objetivos del estudio RIBhuC (del ingles Respiratory Infections in Bhutanese Children). Se detallan los principales hallazgos del estudio RIBhuC en los artículos 3 a 6 de esta tesis. El estudio RIBhuC se llevo a cabo entre el 1 de julio del 2017 y el 30 de junio del 2018 en el Hospital Nacional de Referencia Jigme Dorji Wangchuck, en Thimphu. Se recluto prospectivamente a todos los ninos entre 2 y 59 meses ingresados por neumonia clinica segun los criterios de la OMS, siempre que los padres o cuidadores aceptaran participar en el estudio. Al ingreso, se realizo un examen fisico completo incluyendo mediciones antropometricas y toma de signos vitales. Se recogieron datos demograficos y clinicos mediante entrevistas con los familiares y a partir del expediente medico. Se realizo una radiografia de torax anteroposterior en las primeras 24 horas del ingreso y se 20 clasificaron los ninos segun los criterios radiologicos de la OMS. Se recogieron muestras de sangre en el momento del reclutamiento, o lo antes posible despues del reclutamiento, para analisis de hematologia y bioquimica, cultivo bacteriano, e identificacion de Streptococcus pneumoniae mediante la reaccion en cadena de la polimerasa en tiempo real a partir de dos gotas de sangre recogidas en papel de filtro. Tambien se midieron los niveles plasmaticos de once biomarcadores de respuesta del huesped, incluyendo seis marcadores de activacion endotelial e inmune: la interleucina-6 (IL-6), la interleucina-8 (IL-8), el receptor de activacion soluble expresado en celulas mieloides-1 (sTREM-1), el receptor soluble del factor de necrosis tumoral 1 (sTNFR1), la angiopoyetina-2 (Angpt-2), y la tirosina quinasa-1 soluble similar a fms (sFlt1). Finalmente, se recogieron muestras respiratorias mediante lavado nasofaringeo para la identificacion molecular de 17 virus respiratorios y 4 bacterias atipicas, asi como para la deteccion y tipificacion capsular neumococica. El tercer artículo describe el perfil etiologico y las caracteristicas demograficas y clinicas de esta cohorte de ninos butaneses ingresados con neumonia clinica. El cuarto artículo presenta la prevalencia de portadores nasofaringeos neumococicos y los serotipos neumococicos circulantes entre los ninos butaneses ingresados con neumonia clinica antes de la introduccion de la vacuna antineumococica conjugada en el pais. Comparamos la prevalencia y tipos de virus respiratorios entre ninos con y sin colonizacion nasofaringea neumococica para contribuir a la comprension de la interaccion entre la colonizacion nasofaringea neumococica y las coinfecciones virales. El quinto artículo describe los hallazgos radiologicos y evalua las diferencias en cuanto a caracteristicas demograficas, etiologicas, clinicas y niveles de biomarcadores segun las caracteristicas radiologicas. En este articulo, tambien se evalua la utilidad de biomarcadores para diferenciar entre neumonia bacteriana y viral, considerando el hallazgo de neumonia radiologica (condensacion, derrame pleural o ambos) como indicador de neumonia bacteriana. El sexto y último artículo de esta tesis evalua el rendimiento de caracteristicas clinicas, pruebas de laboratorio, escalas de puntuacion clinica y biomarcadores para estratificar el riesgo pronostico de los ninos con neumonia clinica en el momento del ingreso y predecir su resultado clinico