Details

Autor(en) / Beteiligte

• Abubakar, Mohammed Mansur
• Adamu, Bashir Zak
• Abubakar, Muhammad Zaharaddeen

Titel

Pneumonia Classification Using Hybrid CNN Architecture

Ist Teil von

• 2021 International Conference on Data Analytics for Business and Industry (ICDABI), 2021, p.520-522

Ort / Verlag

IEEE

Erscheinungsjahr

2021

Quelle

IEEE Electronic Library Online

Beschreibungen/Notizen

• In this study, we propose a custom-built deep learning model for detecting pneumonia conditions by analyzing radiographs. The hybrid CNN model is trained to classify distinguishable traces of pneumonia into three (3) different categories; bacterial, normal, and viral pneumonia x-ray images. Experiments were conducted using the proposed hybrid CNN approach which is made of several convolution blocks with custom weights and multiple fully connected layers for accurate classification. The proposed deep learning model resulted in an accuracy of 92.9%, which makes it the top-ranking model in comparison to other models in this research.