Advancing Pneumonia Diagnosis: Hybrid and Optimal Deep CNN Model for Chest Image Classification

Pneumonia is a significant worldwide health issue, with numerous etiologies, such as bacteria, fungi, and viruses, contributing to its challenging diagnosis. Computed Tomography (CT) imaging is crucial for diagnosing pneumonia because it provides detailed information on lung abnormalities. Many hospitals have adopted CT scans during the pandemic to identify lung diseases caused by respiratory infections. Not only is it more expensive, but it is not as widely accessible. Early, precise, and cost-effective identification of pneumonia problems is necessary for improved treatment outcomes. Pneumonia-Plus, which uses Convolutional Neural Network (CNN) architecture, was trained on a large set of annotated CT scans that included bacterial, fungal, and viral pneumonia cases. This work aims to optimize the system to recognize distinct features associated with multiple independent causes of pneumonia. This work utilized a Deep Learning (DL) model, dubbed Pneumonia-Plus, to categorize viral, bacterial, and fungal pneumonia using chest photos. The experiment shows that Pneumonia-Plus performs better overall by accurately classifying pneumonia cases. Most importantly, the model achieves the best accuracy and recall rates while showing resilience in distinguishing between bacterial, viral, and fungal pneumonia.