On-Device Pneumonia Diagnosis: Benchmarking Deep Learning Architectures on Raspberry Pi

Pneumonia continues to be a serious health problem worldwide, especially for children under the age of five. As reported by the WHO, almost 740,000 children in this age group died from pneumonia in 2019, with most of these cases coming from rural areas where proper diagnostic facilities are limited. Chest X-rays are commonly used to detect pneumonia, but the accuracy of this method depends heavily on the radiologist’s experience. Early diagnosis is important, but not always available in low-resource regions. Because of this, deep learning has gained attention as a possible alternative for automated pneumonia detection. Many models have been proposed, but it is still unclear which one works best on a low-cost, low-power device that can be deployed in rural setups. In this work, we compare several deep learning models—including a simple Sequential CNN, VGG16, ViT, MobileViT Hybrid, EfficientNet-V2S, and MobileNet-V3S—by running and evaluating them on a Raspberry Pi 4B. The goal is to find a model that gives good accuracy while still being efficient enough to run on a small device that can support healthcare in underserved areas.