Ensemble learning based Convolutional Neural Network – Depth Fire for detecting COVID-19 in Chest X-Ray images

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The Unique Corona virus-caused COVID19 deadly disease has gave out a significant dispute to healthcare systems around the world. To stop the virus's transmission and lessen its negative effects on public health, it is crucial to recognise correctly and rapidly those who have COVID19. The application of artificial intelligence (AI) holds the capacity to increase the precision and effectiveness of COVID19 diagnosis. The purpose of the study is to build a reliable AI-based model capable correctly detect COVID19 cases from chest X-ray pictures. A dataset of 16,000 chest X-ray pictures, including COVID19 positive and negative instances, is employed in the investigation. Four convolutional neural network (CNN) the models that previously been trained are employed in the proposed model, and the output of each model is combined using an ensembling technique. The major objective of this project is to develop an accurate and reliable AI-based model to classify COVID19 cases from chest X-ray images. The individuality of this method comes in its capacity to employ data augmentation strategies to enhance model generalisation and prevent overfitting. The accuracy and dependability of the model are moreover advanced by utilising numerous pre-trained CNN models and ensembling methods. The suggested AI-based model's classification accuracy for the five classes (bacterial, COVID19 positive, negative, opacity, and viral), the three classes (COVID19 positive, negative, and healthy), and the two classes (COVID19 positive and negative) was 97.3%, 98.2%, 97.6%, and respectively. The projected model performs better in terms of sensitivity, accuracy and specificity than unconventional techniques that are previously in use. Significant ability may be guided in the suggested AI-based model's ability to recognize COVID19 cases quickly and effectively from X-rays of the chest. This approach can help radiology physicians analyse affected role quickly and correctly, improving patient outcomes and lessening the strain on healthcare systems. To ensure the precision of the diagnosis, it is vital to mention that the model's decisions should be made in consultation with a licenced medical expert.