An All-Inclusive CNN-Based Pneumonia Detection Method

Particularly in children, elderly, immunocompromised adults, pneumonia, a serious respiratory illness including inflammation of the lung air sacs, still causes a significant health risk in this research report. Both efficient treatment and improvement of patient outcomes depend on a proper diagnosis. Among the traditional diagnostic instruments whose subjective fluctuations could result in misdiagnoses and delayed therapy are clinical tests and chest X-rays. This paper investigates a CNN-based method to increase chest X-ray image pneumonia detection accuracy. We use a dataset of 624 high-resolution X-ray pictures from Kaggle to implement the CNN model under MobileNetV2 architecture. With a fantastic total accuracy of 92.6%, the model achieves outstanding accuracy (91%), recall (94%), and F1-score (92.5%). Design, performance evaluation of CNN models, and training technique expose their ability to differentiate between normal lung states and pneumonia. By automating image processing, lowering diagnosis mistakes, and enhancing clinical decision-making, this approach presents enormous opportunities to assist radiologists. Such developments guarantee excellent healthcare, help to improve world access to healthcare and inspire medical technical innovation. Future work will concentrate on improving CNN architecture, strengthening data augmentation methods, and investigating hybrid models to incrementally improve generalizability and diagnostic accuracy.