Üstün pnömoni tespiti için bok böceği ve fick yasası kullanılarak hibrit optimizasyonla geliştirilmiş yeni bir derin öğrenme çerçevesi

Pneumonia is a leading cause of global mortality, particularly among children and the elderly, and is characterized by inflammation of the air sacs in the lungs. Early and accurate detection is crucial to mitigating its spread and improving patient outcomes. Traditional diagnostic methods, including physical exams, blood tests, and chest X-ray analysis by radiologists, are effective but often time-intensive, resource-heavy, and prone to human error. These limitations underscore the need for advanced, efficient diagnostic solutions. This study leverages deep learning techniques, specifically Convolutional Neural Networks (CNN) and MobileNet architectures, to enhance pneumonia detection using chest X-ray images. Deep learning excels in image recognition tasks through automatic feature extraction, making it a powerful tool for medical diagnostics. The research applies these models to a large dataset of chest X-rays labeled as NORMAL or PNEUMONIA, utilizing rigorous preprocessing techniques, including normalization and augmentation, to improve data quality and diversity. Hybrid optimization techniques inspired by the dung beetle optimizer and Fick's law of diffusion are employed to optimise model performance, effectively navigating complex parameter spaces. Evaluation metrics such as accuracy, precision, recall, and F1-score validate the models. Results demonstrate the enhanced MobileNet model's superior performance, achieving an accuracy of 98.19%, significantly outperforming the CNN baseline. This highlights the potential of deep learning and innovative optimization methods in advancing pneumonia diagnostics. The findings emphasize the broader implications of artificial intelligence in healthcare, offering a pathway for developing robust diagnostic tools that expedite accurate and timely disease detection. This research contributes to alleviating the global pneumonia burden and underscores the need for continued innovation in machine learning to enhance medical outcomes.