Pneumonia Detection on Chest X-ray Using Deep Convolutional Neural Networks (original) (raw)
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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.
Deep Learning Based Efficient Pneumonia Detection in Chest X-Ray Images
Irish Interdisciplinary Journal of Science & Research (IIJSR), 2023
Medical motion plays an essential role in clinical treatment. The deep neural network is an emerging machine learning method that has proven its potential for different classification tasks. Notably, the convolutional neural network dominates with the best results on varying image classification tasks. However, medical image datasets are hard to collect because it needs a lot of professional expertise to label them. Although it can be detected and treated with very less sophisticated instruments and medication. Therefore, this paper how to apply the Convolutional Neural Network (CNN) based algorithm on a chest X-ray dataset to classify pneumonia. 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. Deep learning-based methods have shown good generalization traits over various problem domains, which prompts researchers around the globe to work tirelessly and come up with more efficient and effective models than earlier. However, this robust nature comes at the cost of high computational resources and, in general, it requires a huge amount of data to train the model efficiently. The latter requirement sometimes cannot be fulfilled, especially in the biomedical field. The main goal is to propose a solution for the above-mentioned problem, using a novel deep neural network architecture. The proposed novelty consists of the use of dropout in the convolutional part of the network. The proposed method was trained and tested on a set of labeled images. This project aims to introduce a deep learning technology based on the computational neural network, which can realize automatic diagnosis of patients with pneumonia in X-ray images.
Detection of Pneumonia Using Chest X-Ray Images with Deep Learning Techniques-Review
International Journal of Scientific Research in Science and Technology, 2023
Pneumonia is a prevalent respiratory infection that requires timely and accurate detection for effective treatment and improved patient outcomes. Traditional methods of pneumonia diagnosis, such as manual interpretation of chest X-ray images, are subjective and time-consuming. This research paper examines the utilization of deep learning techniques for the detection of pneumonia using chest X-ray images. The study delves into the challenges encountered within this domain, including the scarcity of annotated datasets, class imbalance, interpretability of model predictions, generalization, and integration into clinical practice. Various methodologies and solutions are discussed to mitigate these challenges and enhance the performance of deep learning models. The literature review encompasses investigations on CNN-based frameworks, transfer learning, dataset creation, and interpretability techniques. The paper underscores the significance of data preprocessing approaches, such as image resizing, normalization, and augmentation. In summary, this research paper provides valuable insights into the potential of deep learning in pneumonia detection and establishes a basis for further advancements in this field.
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
Pneumonia Disease Detection Using Deep Learning Methods from Chest X-Ray Images: Review
International Journal of Advanced Trends in Computer Science and Engineering , 2021
Pneumonia is an infection-related condition under which the bronchi get damaged and clogged, decreasing oxygen diffusion and causing coughing and difficulty breathing. It can cause a range of symptoms, but it is more common in vulnerable populations. Pneumonia is the leading risk factor for mortality around the globe. Annually, pneumonia kills a significant percentage of youngsters around the globe. In 2016, an estimated one million cases of bronchitis were confirmed in kids under the age of five, with 880,000 deaths. The causes and symptoms of pneumonia are explained in this paper. There are several challenges occurs during detection of pneumonia from x-ray images. Some challenges are explained in this paper. The comparison of existing method of pneumonia detection and classification is depicted with limitations. Various deep learning based architectures are discussed in this paper. The pre-trained models based on deep learning framework such as InceptionV-4, CNN, ResNet50, VGG16, VGG19.
Pneumonia Detection through X-Ray Using Deep Learning
2020
Pneumonia Detection through X-Ray Using Deep Learning is a web application which is used to detect the presence of Pneumonia from a collection of chest X-ray samples. Remarkable classification performance is achieved using methods that rely solely on transfer learning approaches or traditional handcrafted techniques. We constructed a convolutional neural network (CNN) model that extract features from a given chest X-ray image then it classifies it to determine if a person is infected with pneumonia. Reliability and interpretability challenges can be mitigated by this model that are often faced when dealing with medical imagery Key Word: Pneumonia, Detection, X-ray, Convolutional Neural Network (CNN), infected, Deep Learning and medical -------------------------------------------------------------------------------------------------------------------------------------Date of Submission: 18-02-2020 Date of Acceptance: 02-03-2020 --------------------------------------------------------...
Review on Pneumonia Detection from Chest X-Ray using Deep Learning Approach
International Journal for Research in Applied Science and Engineering Technology IJRASET, 2020
Machine Learning (ML) provides various techniques and tools that can help solving diagnostic and prognostic problems in a variety of medical fields. Machine learning is being used for the analysis of the importance of clinical parameters and their combinations for diagnosis, e.g. prediction of disease progression, extracting medical information for outcome analysis, therapy planning and support, and for the patient management. ML is often used for data processing, such as data regularity identification by careful handling of imperfect data, continuous data analysis used in the Intensive Care Unit, and smart alarming resulting in accurate and efficient monitoring.ML can detect patterns of certain diseases in patient electronic healthcare records, and inform physicians of any anomalies. Chest X-rays are used to diagnose various diseases. Multiple diseases can be diagnosed from pneumonia to lung nodules using Deep Learning. A pre-trained ResNet-50 model is retrained with the use of different datasets of chest x-ray images. Notwithstanding major differences in datasets, ResNet-50 based diagnostic model is considered useful for pneumonia diagnosis. The trained model has achieved a 96.76 % accuracy. RSNA dataset, containing five times as many images as the Chest X-ray Image dataset, took very little time to prepare. In addition, both models were able to learn the significant features of pneumonia with a data set size of just % preparation, due to the use of the Transfer Learning technique. Still using deeper networks, the model can be improvised. The research may be expanded to identify and diagnose pneumonia with X-ray images.
Deep Learning Models for Pneumonia Identification and Classification Based on X-Ray Images
Traitement du Signal, 2021
Diagnosis based on chest X-rays is widely used and approved for the diagnosis of various diseases such as Pneumonia. Manually screening of theses X-ray images technician or radiologist involves expertise and time consuming. Addressing this, we propose an automated approach for the diagnosis of pneumonia by assisting doctors in spotting infected areas in the X-ray images. We propose a deep Convolutional Neural Network (CNN) model for efficiently detecting the presence of pneumonia in the X-ray images. The proposed CNN is designed with 5 convolution blocks followed by 4 fully connected layers. In order to boost the performance of the model, we incorporate batch normalization, dynamic dropout, learning rate decay, L2 regularization weight decay along with Adam optimizer and binary Cross-Entropy loss function while training the model using back propagating algorithm. The proposed model is validated on two publicly accessible benchmark datasets, and the experimental studies conducted on ...
Pneumonia detection with Deep Learning
The aim of this project is to classify X-ray images of patients with or without pneumonia. More specifically, we trained a Deep Neural Network(CNN) of different parameters with chest X-ray images of children
Multimedia Tools and Applications, 2021
Pneumonia is a life-threatening respiratory lung disease. Children are more prone to be affected by the disease and accurate manual detection is not easy. Generally, chest radiographs are used for the manual detection of pneumonia and expert radiologists are required for the assessment of the X-ray images. An automatic system would be beneficial for the diagnosis of pneumonia based on chest radiographs as manual detection is time-consuming and tedious. Therefore, a method is proposed in this paper for the fast and automatic detection of pneumonia. A deep learning-based architecture 'MobileNet' is proposed for the automatic detection of pneumonia based on the chest X-ray images. A benchmark dataset of 5856 chest X-ray images was taken for the training, testing, and evaluation of the proposed deep learning network. The proposed model was trained within 3 Hrs. and achieved a training accuracy of 97.34%, a validation accuracy of 87.5%, and a testing accuracy of 94.23% for automatic detection of pneumonia. However, the combined accuracy was achieved as 97.09% with 0.96 specificity, 0.97 precision, 0.98 recall, and 0.97 F-Score. The proposed method was found faster and computationally lesser expensive as compared to other methods in the literature and achieved a promising accuracy.