A Comprehensive CNN-based Approach to Pneumonia Detection

In this research paper, particularly in children, the elderly, and immunocompromised adults, pneumonia, a major respiratory infection including inflammation of the lung air sacs, still causes a substantial health danger. Correct diagnosis is necessary for both effective therapy and improvement of patient outcomes. Clinical exams and chest X-rays are among the conventional diagnostic tools whose subjective variability could lead to misdiagnoses and delayed treatment. This work explores a CNN-based approach to raise pneumonia identification accuracy in chest X-ray images. Using a dataset of 624 high-resolution X-ray images from Kaggle, we apply the CNN model using MobileNetV2 architecture. A remarkable total accuracy of $92.6 \%$ the model achieves great accuracy ($91 \%$), recall ($94 \%$), and F1-score ($92.5 \%$). CNN model performance assessment, design, and training strategy reveal its capacity to distinguish between pneumonia and normal lung states. This approach offers great chances to help radiologists by automating image processing, reducing diagnosis errors, and improving clinical decision-making. Such advances ensure high-quality healthcare, assist in increasing global access to healthcare and motivate medical technological innovation. Future research will focus on enhancing CNN architecture, strengthening data augmentation techniques, and looking at hybrid models to progressively increase generalizability and diagnostic accuracy.