FPGA Implementation of Deep Learning Model Utilizing Different Normalization Algorithms for COVID-19 Diagnosis

Abstract

Normalization is utilized to remove outliers from the dataset and address network bias. In this research, Mean-Variance-Softmax-Rescale (MVSR) and Min-Max normalizations are employed in various combinations for the diagnosis of COVID-19 using a Convolutional Neural Network (CNN)-based Deep Learning (DL) model, aimed at enhancing network accuracy. To accomplish this, the CNN model is developed within the Google Colab environment and trained using a publicly available dataset consisting of chest X-ray images related to COVID-19. The dataset is normalized using different combinations of the MVSR and Min-Max normalization algorithms to compare model accuracy. Each normalized dataset is used for model training, and subsequently, each trained model is integrated into the Kria KV260 Vision AI Starter Kit FPGA for the testing phase. The most accurate results are obtained when both MVSR and Min-Max normalizations are applied simultaneously. This high-performing scenario is re-evaluated with real-time camera and FPGA configuration. During real-time implementations, Min-Max normalization is performed within the Colab environment, while the MVSR normalization algorithm is executed using the Kria KV260 Vision AI Starter FPGA Kit. Experimentally, the highest accuracy is achieved in real-time with the MVSR+Min-Max scenario, reaching 93%. The model's precision, recall, and F1-Score values are determined as 0.91, 0.96, and 0.93, respectively.