Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (19)
  • Open Access


    Modelling a Fused Deep Network Model for Pneumonia Prediction

    M. A. Ramitha*, N. Mohanasundaram, R. Santhosh

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 2725-2739, 2023, DOI:10.32604/csse.2023.030504

    Abstract Deep Learning (DL) is known for its golden standard computing paradigm in the learning community. However, it turns out to be an extensively utilized computing approach in the ML field. Therefore, attaining superior outcomes over cognitive tasks based on human performance. The primary benefit of DL is its competency in learning massive data. The DL-based technologies have grown faster and are widely adopted to handle the conventional approaches resourcefully. Specifically, various DL approaches outperform the conventional ML approaches in real-time applications. Indeed, various research works are reviewed to understand the significance of the individual DL models and some computational complexity… More >

  • Open Access


    Attenuate Class Imbalance Problem for Pneumonia Diagnosis Using Ensemble Parallel Stacked Pre-Trained Models

    Aswathy Ravikumar, Harini Sriraman*

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 891-909, 2023, DOI:10.32604/cmc.2023.035848

    Abstract Pneumonia is an acute lung infection that has caused many fatalities globally. Radiologists often employ chest X-rays to identify pneumonia since they are presently the most effective imaging method for this purpose. Computer-aided diagnosis of pneumonia using deep learning techniques is widely used due to its effectiveness and performance. In the proposed method, the Synthetic Minority Oversampling Technique (SMOTE) approach is used to eliminate the class imbalance in the X-ray dataset. To compensate for the paucity of accessible data, pre-trained transfer learning is used, and an ensemble Convolutional Neural Network (CNN) model is developed. The ensemble model consists of all… More >

  • Open Access


    MDEV Model: A Novel Ensemble-Based Transfer Learning Approach for Pneumonia Classification Using CXR Images

    Mehwish Shaikh1, Isma Farah Siddiqui1, Qasim Arain1, Jahwan Koo2,*, Mukhtiar Ali Unar3, Nawab Muhammad Faseeh Qureshi4,*

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 287-302, 2023, DOI:10.32604/csse.2023.035311

    Abstract Pneumonia is a dangerous respiratory disease due to which breathing becomes incredibly difficult and painful; thus, catching it early is crucial. Medical physicians’ time is limited in outdoor situations due to many patients; therefore, automated systems can be a rescue. The input images from the X-ray equipment are also highly unpredictable due to variances in radiologists’ experience. Therefore, radiologists require an automated system that can swiftly and accurately detect pneumonic lungs from chest x-rays. In medical classifications, deep convolution neural networks are commonly used. This research aims to use deep pre-trained transfer learning models to accurately categorize CXR images into… More >

  • Open Access


    Detection of Omicron Caused Pneumonia from Radiology Images Using Convolution Neural Network (CNN)

    Arfat Ahmad Khan1, Malik Muhammad Ali Shahid2, Rab Nawaz Bashir2, Salman Iqbal2, Arshad Shehzad Ahmad Shahid3, Javeria Maqbool4, Chitapong Wechtaisong5,*

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3743-3761, 2023, DOI:10.32604/cmc.2023.033924

    Abstract COVID-19 disease caused by the SARS-CoV-2 virus has created social and economic disruption across the world. The ability of the COVID-19 virus to quickly mutate and transfer has created serious concerns across the world. It is essential to detect COVID-19 infection caused by different variants to take preventive measures accordingly. The existing method of detection of infections caused by COVID-19 and its variants is costly and time-consuming. The impacts of the COVID-19 pandemic in developing countries are very drastic due to the unavailability of medical facilities and infrastructure to handle the pandemic. Pneumonia is the major symptom of COVID-19 infection.… More >

  • Open Access


    Deep Attention Network for Pneumonia Detection Using Chest X-Ray Images

    Sukhendra Singh1, Sur Singh Rawat2, Manoj Gupta3, B. K. Tripathi4, Faisal Alanzi5, Arnab Majumdar6, Pattaraporn Khuwuthyakorn7, Orawit Thinnukool7,*

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1673-1691, 2023, DOI:10.32604/cmc.2023.032364

    Abstract In computer vision, object recognition and image categorization have proven to be difficult challenges. They have, nevertheless, generated responses to a wide range of difficult issues from a variety of fields. Convolution Neural Networks (CNNs) have recently been identified as the most widely proposed deep learning (DL) algorithms in the literature. CNNs have unquestionably delivered cutting-edge achievements, particularly in the areas of image classification, speech recognition, and video processing. However, it has been noticed that the CNN-training assignment demands a large amount of data, which is in low supply, especially in the medical industry, and as a result, the training… More >

  • Open Access


    WACPN: A Neural Network for Pneumonia Diagnosis

    Shui-Hua Wang1, Muhammad Attique Khan2, Ziquan Zhu1, Yu-Dong Zhang1,*

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 21-34, 2023, DOI:10.32604/csse.2023.031330

    Abstract Community-acquired pneumonia (CAP) is considered a sort of pneumonia developed outside hospitals and clinics. To diagnose community-acquired pneumonia (CAP) more efficiently, we proposed a novel neural network model. We introduce the 2-dimensional wavelet entropy (2d-WE) layer and an adaptive chaotic particle swarm optimization (ACP) algorithm to train the feed-forward neural network. The ACP uses adaptive inertia weight factor (AIWF) and Rossler attractor (RA) to improve the performance of standard particle swarm optimization. The final combined model is named WE-layer ACP-based network (WACPN), which attains a sensitivity of 91.87 ± 1.37%, a specificity of 90.70 ± 1.19%, a precision of 91.01 ± 1.12%, an accuracy of 91.29 ± 1.09%,… More >

  • Open Access


    A New Childhood Pneumonia Diagnosis Method Based on Fine-Grained Convolutional Neural Network

    Yang Zhang1, Liru Qiu2, Yongkai Zhu1, Long Wen1,*, Xiaoping Luo2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.133, No.3, pp. 873-894, 2022, DOI:10.32604/cmes.2022.022322

    Abstract Pneumonia is part of the main diseases causing the death of children. It is generally diagnosed through chest X-ray images. With the development of Deep Learning (DL), the diagnosis of pneumonia based on DL has received extensive attention. However, due to the small difference between pneumonia and normal images, the performance of DL methods could be improved. This research proposes a new fine-grained Convolutional Neural Network (CNN) for children’s pneumonia diagnosis (FG-CPD). Firstly, the fine-grained CNN classification which can handle the slight difference in images is investigated. To obtain the raw images from the real-world chest X-ray data, the YOLOv4… More >

  • Open Access


    Transfer Learning for Chest X-rays Diagnosis Using Dipper Throated Algorithm

    Hussah Nasser AlEisa1, El-Sayed M. El-kenawy2,3, Amel Ali Alhussan1,*, Mohamed Saber4, Abdelaziz A. Abdelhamid5,6, Doaa Sami Khafaga1

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2371-2387, 2022, DOI:10.32604/cmc.2022.030447

    Abstract Most children and elderly people worldwide die from pneumonia, which is a contagious illness that causes lung ulcers. For diagnosing pneumonia from chest X-ray images, many deep learning models have been put forth. The goal of this research is to develop an effective and strong approach for detecting and categorizing pneumonia cases. By varying the deep learning approach, three pre-trained models, GoogLeNet, ResNet18, and DenseNet121, are employed in this research to extract the main features of pneumonia and normal cases. In addition, the binary dipper throated optimization (DTO) algorithm is utilized to select the most significant features, which are then… More >

  • Open Access


    Detection of COVID-19 and Pneumonia Using Deep Convolutional Neural Network

    Md. Saiful Islam, Shuvo Jyoti Das, Md. Riajul Alam Khan, Sifat Momen*, Nabeel Mohammed

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 519-534, 2023, DOI:10.32604/csse.2023.025282

    Abstract COVID-19 has created a panic all around the globe. It is a contagious disease caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), originated from Wuhan in December 2019 and spread quickly all over the world. The healthcare sector of the world is facing great challenges tackling COVID cases. One of the problems many have witnessed is the misdiagnosis of COVID-19 cases with that of healthy and pneumonia cases. In this article, we propose a deep Convolutional Neural Network (CNN) based approach to detect COVID+ (i.e., patients with COVID-19), pneumonia and normal cases, from the chest X-ray images. COVID-19 detection… More >

  • Open Access


    Semantic Pneumonia Segmentation and Classification for Covid-19 Using Deep Learning Network

    M. M. Lotfy1, Hazem M. El-Bakry2, M. M. Elgayar3, Shaker El-Sappagh4,5, G. Abdallah M. I1, A. A. Soliman1, Kyung Sup Kwak6,*

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1141-1158, 2022, DOI:10.32604/cmc.2022.024193

    Abstract Early detection of the Covid-19 disease is essential due to its higher rate of infection affecting tens of millions of people, and its high number of deaths also by 7%. For that purpose, a proposed model of several stages was developed. The first stage is optimizing the images using dynamic adaptive histogram equalization, performing a semantic segmentation using DeepLabv3Plus, then augmenting the data by flipping it horizontally, rotating it, then flipping it vertically. The second stage builds a custom convolutional neural network model using several pre-trained ImageNet. Finally, the model compares the pre-trained data to the new output, while repeatedly… More >

Displaying 1-10 on page 1 of 19. Per Page  

Share Link