EFFECTIVE DETECTION OF LUNG DISEASE FROM X-RAY IMAGES USING CONVOLUTIONAL NEURAL NETWORKS (CNNS) AND VARIOUS ARCHITECTURES

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Naveed Ahmad
Intakhab Alam Qadri
Dr Mehvish Naeem
Dr. Faizan Akbar
Naveed Ahmed
Shahid Iqbal Rai

Keywords

Pneumonia, Deep Learning, Convolutional Neural Network CNN, Image Classification

Abstract

A quick and correct diagnosis is essential for providing patients with quality care since lung illnesses continue to be a major worldwide health problem. The use of Convolutional Neural Networks (CNNs) in the diagnosis of lung illnesses from X-ray images is thoroughly examined in this research using a variety of architectural configurations. The goal is to improve illness classification and identification accuracy and reliability while giving radiologists and other healthcare workers a useful tool. In this study, we used InceptionResNetV2, DenseNet121, VGG16, and Xception, Convolutional neural networks (CNNs) in four distinct configurations. To make a model work better, we need a large dataset. Hence, we gathered the datasets from Kaggle; The First dataset contains 5863 X-ray images, and the second dataset we gathered has 2237 X-ray images from the COVID-19 Radiography Database, both of which are freely accessible on Kaggle. After the dataset collection, we got good accuracy, precision, recall, and f1-score. For the proposed models, 8100 chest X-rays total, including the chest X-rays were preprocessed and trained to detect bacteria, viruses, and normal chest conditions. The Experimental results confirmed that InceptionResNetV2 provides the best classification accuracy as compared to other methods. InceptionResNetV2 achieved the highest accuracy, precision, recall, and F1-Socre of 98.33%, 98.39, 98.21, and 98.30%, respectively. Therefore, this study may be useful in the faster diagnosis of pneumonia by chest X-rays.

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