Abstract
As recent literature suggests, one of the most reliable way for predicting lung diseases is chest X-ray imaging. Numerous works on binary classification of COVID-19 affected from chest X-rays (CXRs) using deep learning (DL) techniques have been found in the literature. However, the study on multiclass lung disease detection is yet quite limited. To fill this gap, this research aims to fine-tune three CNN architectures, such as ResNet-50, VGG-19, and Inception-V3 model, on the chest X-ray dataset to classify the CXRs into five categories, namely COVID-19, tuberculosis (TB), bacterial pneumonia, viral pneumonia, and healthy CXRs. Here, we have implemented different data augmentation techniques and trained our preprocessed data on the modified above-mentioned CNN models. The performance of the fine-tuned CNNs with the pre-trained models are also compared.
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Dan, S., Garai, A., Biswas, S. (2023). Analyzing Lung Diseases Using CNN from Chest X-ray Images. In: Das, A.K., Nayak, J., Naik, B., Vimal, S., Pelusi, D. (eds) Computational Intelligence in Pattern Recognition. CIPR 2022. Lecture Notes in Networks and Systems, vol 725. Springer, Singapore. https://doi.org/10.1007/978-981-99-3734-9_17
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