Enhancing deep learning for pneumonia detection: developing web based solution for Dr. Sumait Hospital in Mogadishu Somalia

In the realm of medical imaging, accurate and efficient detection of pneumonia from chest X-ray images is crucial for timely diagnosis and treatment. This study explores the performance of four deep learning models—Simple CNN, DenseNet121, VGG16, and InceptionV3—using the Kaggle "Chest X-Ray Images (Pneumonia)" dataset, which comprises 5,863 images categorized into normal and pneumonia classes. The methodology included data normalization, augmentation, and training, followed by evaluation based on accuracy, precision, recall, and F1-score. The results revealed that Simple CNN achieved the highest accuracy at 92%, with notable precision (0.95 for normal and 0.90 for pneumonia) and recall (0.83 for normal and 0.97 for pneumonia). VGG16 also performed well with an accuracy of 91%, while DenseNet121 and InceptionV3 had lower performance, with InceptionV3 exhibiting the lowest accuracy (84%) and higher false positive rates. Based on these findings, Simple CNN was chosen for deployment in a Django-based web application hosted on AWS, aimed at improving diagnostic accuracy and supporting healthcare professionals at Dr. Sumait Hospital. The study underscores the efficacy of Simple CNN for clinical applications and suggests future enhancements such as dataset diversification, multi-class classification, real-time processing, and the incorporation of additional clinical data.