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Purpose: Development and assessment the deep learning weakly supervised algorithm for the classification and detection pneumonia via X-ray. Methods: This retrospective study analyzed two publicly available dataset that contain X-ray images of pneumonia cases and normal cases. The first dataset from Guangzhou Women and Children's Medical Center. It contains a total of 5,856 X-ray images, which are divided into training, validation, and test sets with 8:1:1 ratio for algorithm training and testing. The deep learning algorithm ResNet34 was employed to build diagnostic model. And the second public dataset were collated by researchers from Qatar University and the University of Dhaka along with collaborators from Pakistan and Malaysia and some medical doctors. A total of 1,300 images of COVID-19 positive cases, 1,300 normal images and 1,300 images of viral pneumonia for external validation. Class activation map (CAM) were used to location the pneumonia lesions. Results: The ResNet34 model for pneumonia detection achieved an AUC of 0.9949 [0.9910–0.9981] (with an accuracy of 98.29% a sensitivity of 99.29% and a specificity of 95.57%) in the test dataset. And for external validation dataset, the model obtained an AUC of 0.9835[0.9806–0.9864] (with an accuracy of 94.62%, a sensitivity of 92.35% and a specificity of 99.15%). Moreover, the CAM can accurately locate the pneumonia area. Conclusion: The deep learning algorithm can accurately detect pneumonia and locate the pneumonia area based on weak supervision information, which can provide potential value for helping radiologists to improve their accuracy of detection pneumonia patients through X-ray images. Key points: Two publicly available datasets were used for model training and validation, and achieved very good pneumonia detection performance on independent external public data. The DL model based on weak supervision information that can accurately locate the pneumonia area. There is no COVID-19 pneumonia data in the training data, but the DL model can effectively detect COVID-19 pneumonia in independent external public data. [ABSTRACT FROM AUTHOR]

Purpose: X-ray images are viewed as a vital component in emergency diagnosis. They are often used by deep learning applications for disease prediction, especially for thoracic pathologies. Pneumonia, a fatal thoracic disease induced by bacteria or viruses, generates a pleural effusion where fluids are accumulated inside lungs, leading to breathing difficulty. The utilization of X-ray imaging for pneumonia detection offers several advantages over other modalities such as computed tomography scans or magnetic resonance imaging. X-rays provide a cost-effective and easily accessible method for screening and diagnosing pneumonia, allowing for quicker assessment and timely intervention. However, interpretation of chest X-ray images depends on the radiologist’s competency. Within this study, we aim to suggest new elements leading to good interpretation of chest X-ray images for pneumonia detection, especially for distinguishing between viral and bacterial pneumonia. Material and methods: We proposed an interpretation model based on convolutional neural networks (CNNs) and extreme gradient boosting (XGboost) for pneumonia classification. The experimental study is processed through various scenarios, using Python as a programming language and a public database obtained from Guangzhou Women and Children’s Medical Centre. Results: The results demonstrate an acceptable accuracy of 87% within a mere 7 seconds, thereby endorsing its effectiveness compared to similar existing works. Conclusions: Our study provides a model based on CNN and XGboost to classify images of viral and bacterial pneumonia. The work is a challenging task due to the lack of appropriate data. The experimental process allows a better accuracy of 87%, a specificity of 89%, and a sensitivity of 85%. [ABSTRACT FROM AUTHOR]

The objective and automated detection of pneumonia represents a serious challenge in medical imaging, because the signs of the illness are not obvious in CT or X-ray scans. Further on, it is also an important task, since millions of people die of pneumonia every year. The main goal of this paper is to propose a solution for the above mentioned problem, using a novel deep neural network architecture. The proposed novelty consists in the use of dropout in the convolutional part of the network. The proposed method was trained and tested on a set of 5856 labeled images available at one of Kaggle's many medical imaging challenges. The chest X-ray images (anterior-posterior) were selected from retrospective cohorts of pediatric patients, aged between one and five years, from Guangzhou Women and Children's Medical Center, Guangzhou, China. Results achieved by our network would have placed first in the Kaggle competition with the following metrics: 97.2% accuracy, 97.3% recall, 97.4% precision and AUC = 0.982 , and they are competitive with current state-of-the-art solutions. [ABSTRACT FROM AUTHOR]

Pneumonia is the leading cause of death worldwide for children under 5 years of age. For pneumonia diagnosis, chest X-rays are examined by trained radiologists. However, this process is tedious and time-consuming. Biomedical image diagnosis techniques show great potential in medical image examination. A model for the identification of pneumonia, trained on chest X-ray images, has been proposed in this paper. The compound scaled ResNet50, which is the upscaled version of ResNet50, has been used in this paper. ResNet50 is a multilayer layer convolution neural network having residual blocks. As it was very difficult to obtain a sufficiently large dataset for detection tasks, data augmentation techniques were used to increase the training dataset. Transfer learning is also used while training the models. The proposed model could help in detecting the disease and can assist the radiologists in their clinical decision-making process. The model was evaluated and statistically validated to overfitting and generalization errors. Different scores, such as testing accuracy, F1, recall, precision and AUC score, were computed to check the efficacy of the proposed model. The proposed model attained a test accuracy of 98.14% and an AUC score of 99.71 on the test data from the Guangzhou Women and Children's Medical Center pneumonia dataset. [ABSTRACT FROM AUTHOR]

The squash function in capsule networks (CapsNets) dynamic routing is less capable of performing discrimination of non-informative capsules which leads to abnormal activation value distribution of capsules. In this paper, we propose vertical squash (VSquash) to improve the original squash by preventing the activation values of capsules in the primary capsule layer to shrink non-informative capsules, promote discriminative capsules and avoid high information sensitivity. Furthermore, a new neural network, (i) skip-connected convolutional capsule (S-CCCapsule), (ii) Integrated skip-connected convolutional capsules (ISCC) and (iii) Ensemble skip-connected convolutional capsules (ESCC) based on CapsNets are presented where the VSquash is applied in the dynamic routing. In order to achieve uniform distribution of coupling coefficient of probabilities between capsules, we use the Sigmoid function rather than Softmax function. Experiments on Guangzhou Women and Children's Medical Center (GWCMC), Radiological Society of North America (RSNA) and Mendeley CXR Pneumonia datasets were performed to validate the effectiveness of our proposed methods. We found that our proposed methods produce better accuracy compared to other methods based on model evaluation metrics such as confusion matrix, sensitivity, specificity and Area under the curve (AUC). Our method for pneumonia detection performs better than practicing radiologists. It minimizes human error and reduces diagnosis time. [ABSTRACT FROM AUTHOR]

Pneumonia is one of the major diseases that cause a lot of deaths all over the world. Determining pneumonia from chest X-ray (CXR) images is an extremely difficult and important image processing problem. The discrimination of whether pneumonia is of bacterium or virus origin has also become more important during the pandemic. Automatic determination of the presence and origin of pneumonia is crucial for speeding up the treatment process and increasing the patient’s survival rate. In this study, a convolutional neural network (CNN) framework is proposed for detection of pneumonia from CXR images. Two different binary CNNs and a triple CNN are used for determining: (i) normal or pneumonia, (ii) pneumonia of bacterium or virus origin, and (iii) normal or bacterial pneumonia or viral pneumonia. In this approach, CNNs are trained with Walsh functions to extract the features from CXR images, and minimum distance classifier instead of a fully connected neural network is employed for classification purpose. Training with Walsh functions maintains the within-class scattering to be low, and between-class scattering to be high. Preferring the minimum distance classifier reduces the number of nodes used and also allows the network to be controlled with fewer hyperparameters. These approaches bring some advantages to the system designed for the classification process: (i) easy determination of hyperparameters, (ii) achieving higher classification performance, and (iii) use of fewer neurons. The proposed smallsize CNN model was applied to CXR images from 1- to 5-year-old children provided by the Guangzhou Women’s and Children’s Medical Center (GWCMC). Three experiments have been conducted to improve the classification performance: (i) the effect of different sizes of input images on the performance of the network was investigated, (ii) training set was augmented by randomly flipping left to right or down to up, by adding Gaussian noise to the images, by creating negative images randomly, and by changing image brightness randomly (iii) instead of RGB CXR images, gray component of the original image and four 2D wavelet images were given as input to the network. In these experiments, no major changes were observed in the classification results obtained by using the proposed CNNs. The proposed method has achieved 100% accuracy for normal or pneumonia, 92% for pneumonia of bacterium or virus origin, and 90% for normal or bacterial pneumonia or viral pneumonia. It is observed that higher classification performances were obtained with approximately five times less parameters compared to the networks that gave the best results in the literature. Thus, the applied CNN model is promising in medicine and can help experts make quick and accurate decisions. [ABSTRACT FROM AUTHOR]

Pneumonia is among the top diseases which cause most of the deaths all over the world. Virus, bacteria and fungi can all cause pneumonia. However, it is difficult to judge the pneumonia just by looking at chest X-rays. The aim of this study is to simplify the pneumonia detection process for experts as well as for novices. We suggest a novel deep learning framework for the detection of pneumonia using the concept of transfer learning. In this approach, features from images are extracted using different neural network models pretrained on ImageNet, which then are fed into a classifier for prediction. We prepared five different models and analyzed their performance. Thereafter, we proposed an ensemble model that combines outputs from all pretrained models, which outperformed individual models, reaching the state-of-the-art performance in pneumonia recognition. Our ensemble model reached an accuracy of 96.4% with a recall of 99.62% on unseen data from the Guangzhou Women and Children's Medical Center dataset. [ABSTRACT FROM AUTHOR]

Pneumonia causes the death of around 700,000 children every year and affects 7% of the global population. Chest X-rays are primarily used for the diagnosis of this disease. However, even for a trained radiologist, it is a challenging task to examine chest X-rays. There is a need to improve the diagnosis accuracy. In this work, an efficient model for the detection of pneumonia trained on digital chest X-ray images is proposed, which could aid the radiologists in their decision making process. A novel approach based on a weighted classifier is introduced, which combines the weighted predictions from the state-of-the-art deep learning models such as ResNet18, Xception, InceptionV3, DenseNet121, and MobileNetV3 in an optimal way. This approach is a supervised learning approach in which the network predicts the result based on the quality of the dataset used. Transfer learning is used to fine-tune the deep learning models to obtain higher training and validation accuracy. Partial data augmentation techniques are employed to increase the training dataset in a balanced way. The proposed weighted classifier is able to outperform all the individual models. Finally, the model is evaluated, not only in terms of test accuracy, but also in the AUC score. The final proposed weighted classifier model is able to achieve a test accuracy of 98.43% and an AUC score of 99.76 on the unseen data from the Guangzhou Women and Children's Medical Center pneumonia dataset. Hence, the proposed model can be used for a quick diagnosis of pneumonia and can aid the radiologists in the diagnosis process. [ABSTRACT FROM AUTHOR]