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COVID-19 is still disrupting many parts of the world. A rapid and accurate diagnosis solution is needed to combat the pandemic. As a part of the AIRBiS(AI-Enabled Real-time Pneumonia Detection Bio-medical System), this work conduct hardware acceleration to speed up the diagnosis. We found that more than 90% of the current diagnosis time is spent on the convolution function and have conducted three methods to speed up the convolution operations. Firstly, by applying the Winograd algorithm on convolution layers, the multiplication operations of the matrices can be decreased, which speeds up the calculation. The next step is to improve the data exchange speed between the FPGA and CPU by replacing the normal buffer with LineBuffer. We also tried to improve the calculation speed by quantization, reducing the number of bits used for the filter and the input image. The FPGA board we used for this research is ZCU102. The application used for high-level synthesis is Xilinx SDSoC 2019.1. Using the mentioned approaches, we improved the inference speed from 106ms to 22.2ms per image.

Recent years have witnessed a rapid growth of Artificial Intelligence (AI) in biomedical fields. However, an accurate and secure system for pneumonia detection and diagnosis is urgently needed. We present the optimization and implementation of a collaborative learning algorithm for an AI-Enabled Real-time Biomedical System (AIRBiS), where a convolution neural network is deployed for pneumonia (i.e., COVID-19) image classification. With augmentation optimization, the federated learning (FL) approach achieves a high accuracy of 95.66%, which outperforms the conventional learning approach with an accuracy of 94.08%. Using multiple edge devices also reduces overall training time.

COVID-19 is currently on the rage all over the world and has become a pandemic. To efficiently handle it, accurate diagnosis and prompt reporting are essential. The AI-Enabled Real-time Biomedical System (AIRBiS) research project aims to develop a system that handles diagnosis using chest X-ray images. The project is divided into UI, network, software and hardware. This work focuses on the hardware, which uses CNN technology to create a model that determines the presence of pneumonia. This CNN model is designed on an FPGA to speed up diagnostic results. The FPGA increases the flexibility of circuit design, allowing us to optimize the computational processing during data transfer and CNN implementation, reducing the diagnostic measurement time for a single image.

Artificial Intelligence (AI) has recently become a topic of study in different applications, including healthcare, in which timely detection of anomalies can play a vital role in patients health monitoring. The coronavirus disease 2019 (COVID-19), caused by the SARS-CoV-2 virus, colloquially known as the Coronavirus, disrupts large parts of the world. The standard way to test for COVID-19 is Reverse Transcription Polymerase Chain Reaction (RT-PCR), which uses collected samples from the patient. This paper presents an efficient convolution neural network software implementation for COVID-19 and other pneumonia disease detection targeted for an AI-enabled smart biomedical diagnosis system (AIRBiS). From the evaluation results, we found that the classification accuracy of the abnormal (COVID-19 and pneumonia) test dataset is over 97.18%. On the other hand, the accuracy of the normal is no more than 71.37%. We discussed the possible problems and proposals for further optimization.