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Enhancing Viral Pneumonia Diagnosis Accuracy Using Transfer Learning and Ensemble Technique from Chest X-ray Images
Chandrashekhar Uppin, Usman Bello Abubakar
Pages - 43 - 53     |    Revised - 30-06-2022     |    Published - 01-08-2023
Volume - 17   Issue - 3    |    Publication Date - August 2023  Table of Contents
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KEYWORDS
Viral Pneumonia, Chest X-ray, Transfer Learning, Ensemble Methods, Medical Image Classification, Artificial Intelligence.
ABSTRACT
Pneumonia is an acute pulmonary infection that can be caused by bacteria, viruses, or fungi. It infects the lungs, causing inflammation of the air sacs and pleural effusion: a condition in which the lung is filled with fluid. The diagnosis of pneumonia is tasking as it requires a review of Chest X-ray (CXR) by specialists, laboratory tests, vital signs, and clinical history. Utilizing CXR is an important pneumonia diagnostic method for the evaluation of the airways, pulmonary parenchyma, and vessels, chest walls among others. It can also be used to show changes in the lungs caused by pneumonia. This study aims to employ transfer learning, and ensemble approach to help in the detection of viral pneumonia in chest radiographs. The transfer learning model used was Inception network, ResNet-50, and InceptionResNetv2. With the help of our research, we were able to show how well the ensemble technique, which uses InceptionResNetv2 and the utilization of the Non-local Means Denoising algorithm, works. By utilizing these techniques, we have significantly increased the accuracy of pneumonia classification, opening the door for better diagnostic abilities and patient care. For objective labeling, we obtained a selection of patient chest X-ray images. In this work, the model was assessed using state-of-the-art metrics such as accuracy, sensitivity, and specificity. From the statistical analysis and scikit learn python analysis, the accuracy of the ResNet-50 model was 84%, the accuracy of the inception model was 91% and lastly, the accuracy of the InceptionResNetv2 model was 96%.
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Dr. Chandrashekhar Uppin
Department of Computer Science, Baze University Abuja - Nigeria
cvuppin@gmail.com
Dr. Usman Bello Abubakar
Department of Computer Science, Baze University Abuja - Nigeria


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