Hybridizing Convolutional Neural Network for Classification of Lung Diseases

Mukesh Soni, S. Gomathi, Pankaj Kumar, Prathamesh P. Churi, Mazin Abed Mohammed and Akbal Omran Salman
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Mukesh Soni: Jagran Lakecity University, India
S. Gomathi: UK International Qualifications, Ltd., India
Pankaj Kumar: Noida Institute of Engineering and Technology, Greater Noida, India
Prathamesh P. Churi: NMIMS University, India
Mazin Abed Mohammed: University of Anbar, Iraq
Akbal Omran Salman: Middle Technical University, Iraq

International Journal of Swarm Intelligence Research (IJSIR), 2022, vol. 13, issue 2, 1-15

Abstract: Pulmonary disease is widespread worldwide. There is persistent blockage of the lungs, pneumonia, asthma, TB, etc. It is essential to diagnose the lungs promptly. For this reason, machine learning models were developed. For lung disease prediction, many deep learning technologies, including the CNN, and the capsule network, are used. The fundamental CNN has low rotating, inclined, or other irregular image orientation efficiency. Therefore by integrating the space transformer network (STN) with CNN, we propose a new hybrid deep learning architecture named STNCNN. The new model is implemented on the dataset from the Kaggle repository for an NIH chest X-ray image. STNCNN has an accuracy of 69% in respect of the entire dataset, while the accuracy values of vanilla grey, vanilla RGB, hybrid CNN are 67.8%, 69.5%, and 63.8%, respectively. When the sample data set is applied, STNCNN takes much less time to train at the cost of a slightly less reliable validation. Therefore both specialists and physicians are simplified by the proposed STNCNN System for the diagnosis of lung disease.

Date: 2022
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