Efficient Categorization of Pneumonia Diagnosis Using Low-Power Embedded Devices

The lungs are integral to facilitating the respiratory processes crucial for human survival. However, lung infections can pose severe threats to human health. In medical diagnostics, numerous tools and techniques exist for assessing and diagnosing lung-related issues. Among these, Chest X-ray images have emerged as a widely favoured choice due to their distinct advantages, such as accessibility and the ability to provide valuable insights into pulmonary conditions. This work is dedicated to developing a robust multiclass category system for Chest X-ray images. A notable feature of this work is its emphasis on the efficient execution of the categorization work on a low-power heterogeneous embedded device, demonstrating the potential for practical applications in resource-constrained environments. One of this study's key highlights lies in exploring different optimization algorithms and their impact on category accuracy. The study conducts comprehensive experiments utilizing four prominent optimizers: Adam, Adamax, RMSprop, and SGD. Through these experiments, we observed that the proposed modified design attained the highest rate of accuracy, 97.16% when the Adam optimizer was employed. This outcome underscores the significance of optimizer selection in developing accurate and reliable diagnostic models for Chest X-ray image categorization, ultimately contributing to advancements in medical imaging and healthcare.