Publication

Abstract : This research addresses the challenges of real-time medical image analysis by developing a system that combines the power of FPGAs and machine learning. The framework is designed for advanced medical imaging, specifically focusing on MRI analysis. The system utilizes the parallel processing capabilities of FPGA to accelerate critical tasks such as noise reduction, image reconstruction using Fast Fourier Transform (FFT), and edge detection for improved feature extraction. In parallel, a Convolutional Neural Network (CNN), implemented externally, enhances diagnostic accuracy by performing automated abnormality detection. The framework emphasizes efficient FPGA resource utilization, real-time processing, and seamless integration with external machine learning models, highlighting its potential to provide precise and rapid diagnostic support in healthcare.