Effective detection of COVID-19 outbreak in chest X-Rays using fusionnet model

Yenurkar, Ganesh; Mal, Sandip

doi:10.1080/13682199.2023.2173543

Cited by 6 publications

(4 citation statements)

References 45 publications

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“…Goyal et al [1] introduce a framework that combines neural networks and machine learning, particularly RNN and LSTM, for diagnosing lung diseases from chest X-rays by extracting and normalizing visual, shape, texture, and intensity features. Yenurkar et al [2] propose the FusionNet Model for omicron detection from CXR images, achieving high classification accuracy through preprocessing, feature extraction using the Parallel Attention Layer (PAL), and optimal feature selection with the Entropy Correlation score and Emperor Salp Algorithm. J Zhang et al [3] present the confidence-aware anomaly detection (CAAD) model, a oneclass anomaly detection approach for viral pneumonia detection, demonstrating comparable performance to radiologists.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Deep learning, particularly through convolutional neural network (CNN) models, has demonstrated success in image recognition and classification, highlighting its potential in lung infection diagnosis and the promise of future advancements. Although deep neural networks offer potential for lung infection detection, the lack of peerreviewed research comparing the efficacy of different deep learning systems in identifying lung infection abnormalities remains a gap in the field [2]. The challenge in addressing lung infections, revolves around developing an accurate and efficient detection method using deep learning models.…”

Section: Introductionmentioning

confidence: 99%

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Cascaded Deep Learning Model for Detecting Lung Infections Using Chest X-Rays

Chaturvedi,

Soni

2024

IJOSCIENCE

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This work focuses on efforts for accurately predicting lung diseases like omicron and pneumonia using chest X-ray imaging, a reliable method in this domain. The work adopts a transfer learning model for lung infection predictions from chest X-ray images. The proposed architecture encompasses both training and testing functions, with key steps including pre-processing, deep feature extraction, and classification. Initially, each X-ray image is enhanced through digital filtering for quality improvement. These processed images are then input into a robust, step-wise learning model that efficiently facilitates the automatic learning of features. The highlight of this approach is the Cascaded learning model, which not only achieves a high accuracy rate of 99% but also significantly reduces computational complexity. This is evidenced by a lower number of training parameters, making the model both more efficient and lightweight, and hence more practical for clinical applications in differentiating between omicron and pneumonia.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cascaded Deep Learning Model for Detecting Lung Infections Using Chest X-Rays

Chaturvedi,

Soni

2024

IJOSCIENCE

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“…Achieving an F1‐score of 99.25%, accuracy, precision, and recall ratings of 98.71%, 98.89%, and 99.63% for the proposed method. In this study, a unique FusionNet Model is put forth for the accurate categorization of COVID‐19 disorders 39 …”

Section: Literature Reviewmentioning

confidence: 99%

“…In this study, a unique FusionNet Model is put forth for the accurate categorization of COVID-19 disorders. 39…”

Section: Literature Reviewmentioning

confidence: 99%

Multifactor data analysis to forecast an individual's severity over novel COVID‐19 pandemic using extreme gradient boosting and random forest classifier algorithms

Yenurkar,

Mal,

Nyangaresi

et al. 2023

Engineering Reports

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AI and machine learning are increasingly often applied in the medical industry. The COVID‐19 epidemic will start to spread quickly over the planet around the start of 2020. At hospitals, there were more patients than there were beds. It was challenging for medical personnel to identify the patient who needed treatment right away. A machine learning approach is used to predict COVID‐19 pandemic patients at high risk. To provide input data and output results that execute the machine learning model on the backend, a straightforward Python Flask web application is employed. Here, the XGBoost algorithm, a supervised machine learning method, is applied. In order to predict high‐risk patients based on their current underlying health issues, the model uses patient characteristics as well as criteria like age, sex, health issues including diabetes, asthma, hypertension, and smoking, among others. The XGBoost model predicts the patient's severity with an accuracy of about 98% after data pre‐processing and training. The most important factors to the models are chosen to be age, diabetes, sex, and obesity. Patients and hospital personnel will benefit from this project's assistance in making timely choices and taking appropriate action. This will let medical personnel decide how much time and space to devote to the COVID‐19 high‐risk patients. providing a treatment that is both efficient and ideal. With this programme and the necessary patient data, hospitals may decide whether a patient need immediate care or not.

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