Optimized Convolution Neural Network-based Model for Detection and Classification of Pulmonary Diseases

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dc.contributor.authorAnthony Taiwo Olajide
dc.contributor.authorRonke Seyi Babatunde
dc.contributor.authorSulaiman Olaniyi Abdulsalam
dc.contributor.authorJumoke Falilat Ajao
dc.contributor.authorRafiu Mope Isiaka
dc.contributor.authorA. J. Kehinde
dc.date.accessioned2025-01-29T04:44:34Z
dc.date.available2025-01-29T04:44:34Z
dc.date.issued2024-06-15
dc.description.abstractPelican 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 study aims to enhance the efficiency of CNN models in diagnosing lung diseases by using the Pelican Optimization Algorithm (POA), and by addressing drawbacks like a lack of flexibility in hyperparameter modifications. 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 resizing, grayscale, and augmentation methods 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.
dc.identifier.urihttps://kwasuspace.kwasu.edu.ng/handle/123456789/3681
dc.language.isoen
dc.publisherLAUTECH Journal of Engineering and Technology
dc.titleOptimized Convolution Neural Network-based Model for Detection and Classification of Pulmonary Diseases
dc.typeArticle
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