Enhanced medical image analysis: leveraging CUDA for fast and accurate Pneumonia detection with optimized CNNs

Abstract

Pneumonia, a known leading child killer and a general health burden, continues to be a major concern due to its high morbidity and mortality rates in the developing world, which calls for prompt and accurate diagnosis. This paper aims at proposing a novel medical image analysis framework that can be used in the enhancement of pneumonia from Chest X-ray images in terms of speed and accuracy. Building on the capability of the Convolutional Neural Networks (CNNs) that have been tuned using NVIDIA CUDA, this strategy enhances the computational capabilities and enables real time analysis. Hence, it meant that we were training a novel deep learning model which was fit for the specific task we were undertaking involving identification of bacterial, viral pneumonia in addition to normal cases. The model finds feature extraction and considers incorporation of advanced layers and/or architectures. By paralleling the codes with Cuda we were able to reduce the time it takes to train and make prediction on models while at the same time not being compromising on the quality of the models. In addition, Our experimental results show that, our CUDA-optimized CNN outperforms and achieve equal or higher accuracy against the traditional methods, all this in a drastically shorter time. There is potential for deploying associated high-resolution diagnostic equipment in clinical environment, specifically in situation where decisions are needed quickly. Our self-contrary contributions signify the effectiveness as well as effectiveness of deep learning and high-performance computing to augment the medical diagnostic technique and would open the area to extensive applications of medical image analysis in the future.