Detecting Covid19 and pneumonia from chest X-ray images using deep convolutional neural networks

Kavya, Nallamothu Sri; shilpa, Thotapalli; Veeranjaneyulu, N.; Priya, D. Divya

doi:10.1016/j.matpr.2022.05.199

Cited by 23 publications

(7 citation statements)

References 29 publications

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“…This detailed literature survey provides an in-depth understanding of the pre-processing and augmentation techniques used in the study and their contribution to improving the accuracy of the proposed framework. Sri Kavya, N. et.al [12] provides valuable insights into the use of deep learning for medical diagnosis and underscores the significance of leveraging CXR images for efficient and accurate detection of COVID-19 and pneumonia. The accuracy of the VGG16 model for detecting pneumonia from chest X-ray images is reported to be 89.34% in the study.…”

Section: Literature Review Sharma and Guleriamentioning

confidence: 99%

A Review on Deep Learning Based Automated Pneumonia Detection using X-ray images

et al. 2024

IJFMR

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This paper provides a comprehensive literature survey on the application of various deep learning models for pneumonia detection using chest X-ray images. It reviews numerous studies that propose different architectures and techniques aimed at enhancing detection accuracy and efficiency. The survey covers the use of methods such as transfer learning and ensemble techniques to mitigate data scarcity and augment model performance. It also discusses the development of advanced algorithms for the precise identification and localization of pneumonia indications. Despite promising results, the survey acknowledges persisting challenges, including the need for high accuracy in medical testing and the limited availability of annotated medical images. This review underscores the transformative potential of deep learning in pneumonia detection and identifies areas for future research.

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Section: Literature Review Sharma and Guleriamentioning

confidence: 99%

A Review on Deep Learning Based Automated Pneumonia Detection using X-ray images

et al. 2024

IJFMR

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“…Haghanifar et al applied lung segmentation and different image enhancement methods to X-ray image data developed a 431-layered, COVID-CXNet (Haghanifar et al 2022 ) DL model using the learning transfer technique and achieved 96.10% success. Kavya et al realized a study on VGG16 and ResNet50 models using X-ray images of individuals to analyze COVID-19 patients from pneumonia patients, and the success of the models was 89.34% and 91.39%, respectively (Kavya et al 2022 ).…”

Section: Related Workmentioning

confidence: 99%

Diagnosis of COVID-19 from blood parameters using convolutional neural network

Erol

Uzbaş

2023

Soft Comput

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Asymptomatically presenting COVID-19 complicates the detection of infected individuals. Additionally, the virus changes too many genomic variants, which increases the virus’s ability to spread. Because there isn’t a specific treatment for COVID-19 in a short time, the essential goal is to reduce the virulence of the disease. Blood parameters, which contain essential clinical information about infectious diseases and are easy to access, have an important place in COVID-19 detection. The convolutional neural network (CNN) architecture, which is popular in image processing, produces highly successful results for COVID-19 detection models. When the literature is examined, it is seen that COVID-19 studies with CNN are generally done using lung images. In this study, one-dimensional (1D) blood parameters data were converted into two-dimensional (2D) image data after preprocessing, and COVID-19 detection was made with CNN. The t-distributed stochastic neighbor embedding method was applied to transfer the feature vectors to the 2D plane. All data were framed with convex hull and minimum bounding rectangle algorithms to obtain image data. The image data obtained by pixel mapping was presented to the developed 3-line CNN architecture. This study proposes an effective and successful model by providing a combination of low-cost and rapidly-accessible blood parameters and CNN architecture making image data processing highly successful for COVID-19 detection. Ultimately, COVID-19 detection was made with a success rate of 94.85%. This study has brought a new perspective to COVID-19 detection studies by obtaining 2D image data from 1D COVID-19 blood parameters and using CNN.

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“…Among these models, AlexNet demonstrated the highest accuracy of 83.968%. Kavya et al [5] [6] presented an innovative neural network named Quaternion Convolution neural network (QCNN) that considers all three color channels of an image as a unified entity, resulting in enhanced feature extraction and classification performance. The authors trained the Quaternion Residual network using an extensive Chest X-Ray dataset, achieving a remarkable classification accuracy of 93.75% and an F-score of 0.94.…”

Section: Related Workmentioning

confidence: 99%

Efficient Pneumonia Diagnosis using a Hybrid Framework Leveraging Deep Learning and Machine Learning Techniques

Muralidhar,

Sathyarajasekaran

et al. 2023

Preprint

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Pneumonia is a potentially fatal lung infection caused by various viral infections. Because pneumonia is like other pulmonary diseases, identifying and treating it on chest X-ray images can be difficult. Therefore, a more reliable method for detecting pneumonia in X-ray images is required. So, an optimized Deep Learning approach is used in this study, in which x-rays of the chest are handled in four stages. The first stage involves performing image enhancement, followed by a data augmentation stage, and the third stage involves feeding the results to deep learning algorithms (CNN, VGG16, InceptionResNetV2, Xception, Resnet50, and hybrid model) in which image features are retrieved for further processing. These extracted features are then utilized in the subsequent step, where they are inputted into various machine learning algorithms, including Logistic Regression, Decision Tree, Random Forest, SVM, and AdaBoost. These algorithms are responsible for classifying and diagnosing the images. To evaluate the proposed approach, a comprehensive dataset comprising 8,217 images (5,259 pneumonia and 2,958 normal) from combined chest X-ray and Mendeley sources was employed. The findings of the experiment reveal that the hybrid model, combined with the SVM classifier, demonstrates exceptional performance in terms of both training and testing accuracy, exceeding 97.70%. In conclusion, the proposed framework successfully leverages the extracted features and machine learning algorithms to achieve highly accurate classification and diagnosis of pneumonia cases. The hybrid model, in conjunction with the SVM classifier, exhibits remarkable performance in accurately identifying pneumonia from chest X-ray images.

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Detecting Covid19 and pneumonia from chest X-ray images using deep convolutional neural networks

Cited by 23 publications

References 29 publications

A Review on Deep Learning Based Automated Pneumonia Detection using X-ray images

A Review on Deep Learning Based Automated Pneumonia Detection using X-ray images

Diagnosis of COVID-19 from blood parameters using convolutional neural network

Efficient Pneumonia Diagnosis using a Hybrid Framework Leveraging Deep Learning and Machine Learning Techniques

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