سيف عبدالحميد مجيد أحمد الكونجي
  • Development of Pneumonia Disease Detection Model Based on Deep Learning Algorithm
  • Abstract

     

    Pneumonia represents a life-endangering and deadly disease that results from a viral or bacterial infection in the human lungs.
    The earlier pneumonia’s diagnosing is an essential aspect in the processes of successful treatment. Recently, the developed
    methods of deep learning that include several layers of processing to comprehend the stratified data representation have
    obtained the best results in various domains, especially in the identification and classification of human diseases. Therefore, for
    improving the systems’ performance for detecting pneumonia disease, there is a requirement for implementing automatic
    models based on deep learning models that have the ability to diagnose the images of chest X-rays and to facilitate the
    detection process of pneumonia novices and experts. A convolutional neural network (CNN) model is developed in this paper
    for detecting pneumonia via utilizing the images of chest X-rays. The proposed framework encompasses two main stages: the
    stage of image preprocessing and the stage of extracting features and image classification. The proposed CNN model provides
    high results of precision, recall, F1-score, and accuracy by 98%, 98%, 97%, and 99.82%, respectively. Regarding the obtained
    results, the proposed CNN model-based pneumonia detection has achieved a better result of consistency and accuracy, and it
    has outperformed the other pretrained deep learning models such as residual networks (ResNet 50) and VGG16. Furthermore,
    it exceeds the recently existing models presented in the literature. Thus, the significant performance of the proposed CNN
    model-based pneumonia detection in all measures of performance can provide effective services of patient care and decrease
    the rates of mortality