Prediction of Pneumonia Using CNN (original) (raw)
Related papers
Detection of Pneumonia in Patients Using Chest X-Ray Images Based on Convolutional Neural Networks
International Journal of Medical and Biomedical Studies, 2022
Pneumonia is a respiratory disease which inflames one or both lungs, and lungs may fill up with fluid. This can cause fever, chills, cough with phlegm or pus. It is a harmful disease that can be fatal in some cases. It is critical to detect pneumonia early, with a model which could help doctors reduce workload and bring in more accuracy in detection of pneumonia. In this work, we propose a model which is designed for the diagnosis of pneumonia that is trained on digital chest X-ray images to accurately diagnose pneumonic lungs. In the author’s opinion, this could benefit the medical field in the near future. Keywords: CNN, Keras, Pneumonia
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...
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.
Predictive Analysis on Pneumonia using CNNs
International Journal for Research in Applied Science and Engineering Technology IJRASET, 2020
Healthcare data analysis has come up as one of the few encouraging researchable domains. Pneumonia affects a large number of world's population. The aim of this briefing is to make a robust decision support system to predict the presence of a disease using the techniques of Machine Learning. Machine Learning is used to analyze the patterns in the data, detect and analyze trends and then make predictions with the help of ML techniques and it's Algorithm. It gives us tools, techniques and methods that can help in solving diagnostic problems in different medical domains e.g. prediction of disease progression, extraction of medical knowledge for outcome research, therapy planning and support, and for the overall patient management. It offers a principled approach for developing automatic and optimal algorithms for biomedical data. This paper focuses on developing a deep neural network which will help predict the presence of pneumonia using chest x-rays. In order to achieve this, Convolutional neural networks have been deployed to increase efficiency and accuracy. Our model will use exact number of epochs that is required to calculate maximum efficiency which has to be given by the model.
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.
An effective framework for identifying pneumonia in healthcare using a Convolutional Neural Network
Pneumonia is now a life-threatening respiratory illness that can affect the lungs. Mainly the aged and children suffer the most. If the right diagnosis is not made, it could be fatal. So early diagnosis is very much needed to save many human lives. For diagnosis purposes Medical imaging, such as a chest x-ray can be utilized effectively and skilled radiologists are needed for this. Due to the blurriness of X-ray images, proper diagnosis can be difficult and time-consuming, even for radiographers with experience. As human judgment is involved, a pneumonia diagnosis may be erroneous. Hence, a deep learning-based automated system can be used to assist the radiographer in taking decisions more precisely and accurately. There have been several existing methods available for diagnosing pneumonia but they have accuracy issues. In this paper, we seek to automate the process of identifying and categorizing cases of pneumonia from CXR images deploying deep CNN. A deep CNN model has been built from scratch which will automate the process and provide high diagnosis performance. After passing through multiple convolutional layers and corresponding max pooling layers, the information is then fed into the dense layers. Lastly, using the sigmoidal function, the classification is performed. The model's performance improves as it simultaneously gains training and reduces loss.
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.
JOIV : International Journal on Informatics Visualization
Pneumonia is one of the world's top causes of mortality, especially for children. Chest X-rays serve an important part in diagnosing pneumonia due to the cost-effectiveness and quick advancement of the technology. Detecting Pneumonia through Chest X-rays (CXR) is a challenging and time-consuming process requiring trained professionals. This issue has been solved by the development of automation technology which is machine learning. Moreover, Deep Learning (DL), a machine learning specification that uses an algorithm that resembles the human brain, can predict more accurately and is now dependable enough to predict pneumonia. As time passes, another Deep Learning improvement has been made to produce a new method called Transfer Learning, that is done by extracting specific layers from some pre-trained network to be used on other datasets, which reduces the training time and improves the model performance. Although numerous algorithms are already available for pneumonia identifica...
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.
MDPI, 2020
Pneumonia is a virulent disease that causes the death of millions of people around the world. Every year it kills more children than malaria, AIDS, and measles combined and it accounts for approximately one in five child-deaths worldwide. The invention of antibiotics and vaccines in the past century has notably increased the survival rate of Pneumonia patients. Currently, the primary challenge is to detect the disease at an early stage and determine its type to initiate the appropriate treatment. Usually, a trained physician or a radiologist undertakes the task of diagnosing Pneumonia by examining the patient's chest X-ray. However, the number of such trained individuals is nominal when compared to the 450 million people who get affected by Pneumonia every year. Fortunately, this challenge can be met by introducing modern computers and improved Machine Learning techniques in Pneumonia diagnosis. Researchers have been trying to develop a method to automatically detect Pneumonia using machines by analyzing and the symptoms of the disease and chest radiographic images of the patients for the past two decades. However, with the development of cogent Deep Learning algorithms, the formation of such an automatic system is very much within the realms of possibility. In this paper, a novel diagnostic method has been proposed while using Image Processing and Deep Learning techniques that are based on chest X-ray images to detect Pneumonia. The method has been tested on a widely used chest radiography dataset, and the obtained results indicate that the model is very much potent to be employed in an automatic Pneumonia diagnosis scheme.