Pneumonia Image Classification Using CNN with Max Pooling and Average Pooling (original) (raw)
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Pneumonia Classification in Lung X-ray Images Using CNN Technique
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022
A fundamental phase in the technique of pneumonia diagnosis is the evaluation and categorization of lung disorders utilizing X-ray pictures, especially during a crucial era such as the COVID19 pandemic, which is a kind of pneumonia. As a result of the growing number of cases, an automated approach with high classification accuracy is required to classify lung disorders. Due to its quickness and effectiveness when it comes to visual recognition tasks, CNN based segmentation has acquired a lot of traction in recent years. We present an implementation of CNN-based classifier techniques utilize a domain adaptation approach to identify pneumonia and analyze the outcomes to choose the best model for the job depending on specific parameters in this paper. There are various models because this is a rapidly growing topic, and we will concentrate on the bestperforming methods relying on their structure, tier length and style, and categorization task assessment criteria. To begin, we look at the existing traditional approaches and supervised learning frameworks for segmentation. Then, depending on the reliability and damage functionality of the constructed models, we undertake a detailed evaluation and analysis. A rigorous examination of the findings is carried out in order to highlight the major concerns that need to be addressed.
Pneumonia Detection Using CNN Through Chest X-Ray
2021
In general, pneumonia affects children under 5 years and adults over 65 years of age which targets the lungs and fills the alveoli (air sacs) with liquid. In this paper, we employ convolutional neural networks (CNNs) of varying configurations on a machine learning based binary classification task with a given dataset of chest X-rays that depicts affected and unaffected cases of pneumonia. This paper primarily focuses on putting forth the performances of different simple CNN architectures and selecting the best architecture based on optimum corresponding minimum loss and maximum accuracy which can serve as a viable tool for physicians and the medicine community to correctly identify and diagnose viral, bacterial, fungal-caused and community acquired pneumonia given only the chest X-ray of the patient.
2021
Pneumonia, an interstitial lung disease, is the leading cause of death in children under the age of five. It accounted for approximately 16% of the deaths of children under the age of five, killing around 880,000 children in 2016 according to a study conducted by UNICEF. Affected children were mostly less than two years old. Timely detection of pneumonia in children can help to fast-track the process of recovery. This paper presents convolutional neural network models to accurately detect pneumonic lungs from chest Xrays, which can be utilized in the real world by medical practitioners to treat pneumonia. Experimentation was conducted on Chest X-Ray Images (Pneumonia) dataset available on Kaggle. The first, second, third and fourth model consists of one, two, three and four convolutional layers, respectively. The first model achieves an accuracy of 89.74%, the second one reaches an accuracy of 85.26%, the third model achieves an accuracy of 92.31%, and lastly, the fourth model achie...
PNEUMONIA DIAGNOSIS USING CHEST X-RAY IMAGES AND CNN
IRJET, 2022
A pneumonia diagnosis system was developed using convolutional neural network (CNN) based feature extraction. InceptionV3 CNN was used to perform feature extraction from chest X-ray images. The extracted feature was used to train three classification algorithm models to predict the cases of pneumonia from the Kaggle dataset. The three models are Support Vector Machines, Neural Networks, and K-Nearest Neighbour The confusion matrix and performance evaluation were presented to represent the sensitivity, accuracy, precision, and specificity of each of the models. Results show that. The sensitivity of the Neural Network model was 84.1 percent, followed by support vector machines (83.5 percent) and the K-Nearest Neighbour Algorithm (83.5 percent) (83.3 percent). The Support vector machines model obtained the highest AUC of all the classification models, at 93.1 percent.
Electronics, 2021
Pneumonia is an inflammation of the lung parenchyma that is caused by a variety of infectious microorganisms and non-infective agents. All age groups can be affected; however, in most cases, fragile groups are more susceptible than others. Radiological images such as Chest X-ray (CXR) images provide early detection and prompt action, where typical CXR for such a disease is characterized by radiopaque appearance or seemingly solid segment at the affected parts of the lung due to inflammatory exudate formation replacing the air in the alveoli. The early and accurate detection of pneumonia is crucial to avoid fatal ramifications, particularly in children and seniors. In this paper, we propose a novel 50 layers Convolutional Neural Network (CNN)-based architecture that outperforms the state-of-the-art models. The suggested framework is trained using 5852 CXR images and statistically tested using five-fold cross-validation. The model can distinguish between three classes: viz viral, bact...
Determination of Pneumonia in X-ray Chest Images by Using Convolutional Neural Network
TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES, 2021
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.
Convolutional Neural Network Based Classification of Patients with Pneumonia using X-ray Lung Images
Advances in Science, Technology and Engineering Systems Journal
Analysis and classification of lung diseases using X-ray images is a primary step in the procedure of pneumonia diagnosis, especially in a critical period as pandemic of COVID-19 that is type of pneumonia. Therefore, an automatic method with high accuracy of classification is needed to perform classification of lung diseases due to the increasing number of cases. Convolutional Neural Networks (CNN) based classification has gained a big popularity over the last few years because of its speed and level of accuracy on the image's classification tasks. Through this article, we propose an implementation a CNNbased classification models using transfer learning technique to perform pneumonia detection and compare the results in order to detect the best model for the task according to certain parameters. As this has become a fast expanding field, there are several models but we will focus on the best outperforming algorithms according to their architecture, length and type of layers and evaluation parameters for the classification tasks. Firstly, we review the existing conventional methods and deep learning architectures used for segmentation in general. Next, we perform a deep performance and analysis based on accuracy and loss function of implemented models. A critical analysis of the results is made to highlight all important issues to improve.
Pneumonia Detection on Chest X-ray Images Using Ensemble of Deep Convolutional Neural Networks
Applied Sciences
Pneumonia is a life-threatening lung infection resulting from several different viral infections. Identifying and treating pneumonia on chest X-ray images can be difficult due to its similarity to other pulmonary diseases. Thus, the existing methods for predicting pneumonia cannot attain substantial levels of accuracy. This paper presents a computer-aided classification of pneumonia, coined Ensemble Learning (EL), to simplify the diagnosis process on chest X-ray images. Our proposal is based on Convolutional Neural Network (CNN) models, which are pretrained CNN models that have been recently employed to enhance the performance of many medical tasks instead of training CNN models from scratch. We propose to use three well-known CNNs (DenseNet169, MobileNetV2, and Vision Transformer) pretrained using the ImageNet database. These models are trained on the chest X-ray data set using fine-tuning. Finally, the results are obtained by combining the extracted features from these three model...
An improvement of the CNN-XGboost model for pneumonia disease classification
Polish Journal of Radiology
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%.
Pneumonia detection in chest X-ray images using compound scaled deep learning model
Automatika, 2021
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.