Deep learning-based technique for lesions segmentation in CT scan images for COVID-19 prediction.

Since 2019, COVID-19 disease caused significant damage and it has become a serious health issue in the worldwide. The number of infected and confirmed cases is increasing day by day. Different hospitals and countries around the world to this day are not equipped enough to treat these cases and stop this pandemic evolution. Lung and chest X-ray images (e.g., radiography images) and chest CT images are the most effective imaging techniques to analyze and diagnose the COVID-19 related problems. Deep learning-based techniques have recently shown good performance in computer vision and healthcare fields. We propose developing a new deep learning-based application for COVID-19 segmentation and analysis in this work. The proposed system is developed based on the context aggregation neural network. This network consists of three main modules: the context fuse model (CFM), attention mix module (AMM) and a residual convolutional module (RCM). The developed system can detect two main COVID-19-related regions: ground glass opacity and consolidation area in CT images. Generally, these lesions are often related to common pneumonia and COVID 19 cases. Training and testing experiments have been conducted using the COVID-x-CT dataset. Based on the obtained results, the developed system demonstrated better and more competitive results compared to state-of-the-art performances. The numerical findings demonstrate the effectiveness of the proposed work by outperforming other works in terms of accuracy by a factor of over 96.23%.

A Convolutional Neural Network to Perform Object Detection and Identification in Visual Large-Scale Data.

In recent years, big data became a hard challenge. Analyzing big data needs a lot of speed precision combination. In this article, we describe a deep learning-based method to deal with big data with a focus on precision and speed. In our case, the data are images that are the hardest type of data to manipulate because of their complex structure that needs a lot of computation power. Besides, we will solve a hard task on images, which is object detection and identification. Thus, every object in the image will be localized and classified according to the range of classes provided by the training data set. To solve this challenge, we propose an approach based on a deep convolutional neural network (CNN). Moreover, CNN is the most used deep learning model in computer vision tasks such as image classification and object recognition because of its power in self-features extraction and provides useful techniques in the prediction of decision-making. Our approach outperforms state-of-the-art models such as R-CNN, Fast R-CNN, Faster R-CNN, and YOLO (you only look once), with 77% of mean average precision on the Pascal_voc 2007 testing data set and a speed of 16.54 FPS using an Nvidia Geforce GTX 960 GPGPU.