Optimized convolution neural network-based model for detection and classification of pulmonary diseases.

T. A. Olajide

T. A. Olajide Kwara State Polytechnic, Ilorin

Keywords: Chest X-Ray, Deep Learning, Convolution neural network, Data augmentation, Pelican optimization, pulmonary disease

Abstract

Pelican Optimization Algorithm-based Convolutional Neural Network (POA-CNN) method for the automated identification of pulmonary disorders such as COVID-19 and pneumonia is proposed in this research. The method makes use of several processing layers in order to comprehend the representation of stratified data. The three primary phases of the model are feature extraction via POA-based hyperparameter optimization, image classification, and image pre-processing. This approach improves existing systems' performance in detecting pulmonary diseases, highlighting the potential of deep learning in identifying and categorizing human diseases. The study uses a resizing, grayscale, and augmentation method to optimize an existing CNN model. A Convolutional Neural Network (CNN) is then applied to classify Pneumonia and Covid-19 cases. The proposed model achieves an accuracy rate of 97.28 and 97.00%, outperforming existing models. This technique is effective in detecting and classifying other pulmonary diseases, and can be used to automatically detect and classify these diseases. Higher accuracy findings show how successful the model is, making it a useful tool for pulmonary illness identification.