Effects of Seawater on Setting Time and Compressive Strength of Concretes with Different Richness

Water is one of the main constituents of concrete. Although many types of water exist, fresh water is the mostly used in concrete industry. Fresh water is expected to be in a great shortage by 2050 according to UN world water development report. Incorporating seawater in concrete mixture can help in the expected problem of scarcity of fresh water. Also, in many cases seawater may be the only available water especially in coastal regions. Many reports mention various possibilities of using seawater in concrete without detrimental effect on concrete properties. In this study another beneficial effect of seawater over tap water was concluded. Setting tests of cement paste mixed with seawater was determined using Vicat apparatus and compared to tap water. Compressive strength tests at the age of 28 days of Portland cement concretes with varied quantity of cement i.e. 300, 350, 400, 450, and 500 kg, and mixed with seawater was also performed and compared to tap water. The results show that seawater affects standard consistency of cement paste and two percent increase was required in order to attain the same consistency as tap water. It shows also seawater slightly accelerates initial setting of cement but the effect is not so pronounced so as to cause a trouble in concrete and final setting time almost remains unaltered. Compressive strength tests show an increase in concrete strength mixed with seawater for all tested mixtures and depending on quantity of cement. It also shows a beneficial effect of seawater on compressive strength of rich concrete with quantity of cement 450 and 500 kg over tap water. Doi: 10.28991/cej-2021-03091695 Full Text: PD

Deep Learning Based Method for Computer Aided Diagnosis of Diabetic Retinopathy

© 2019 IEEE. Diabetic retinopathy (DR) is a retinal disease caused by the high blood sugar levels that may damage and block the blood vessels feeding the retina. In the early stages of DR, the disease is asymptomatic; however, as the disease advances, a possible sudden loss of vision and blindness may occur. Therefore, an early diagnosis and staging of the disease is required to possibly slow down the progression of the disease and improve control of the symptoms. In response to the previous challenge, we introduce a computer aided diagnosis tool based on convolutional neural networks (CNN) to classify fundus images into one of the five stages of DR. The proposed CNN consists of a preprocessing stage, five stage convolutional, rectified linear and pooling layers followed by three fully connected layers. Transfer learning was adopted to minimize overfitting by training the model on a larger dataset of 3.2 million images (i.e. ImageNet) prior to the use of the model on the APTOS 2019 Kaggle DR dataset. The proposed approach has achieved a testing accuracy of 77% and a quadratic weighted kappa score of 78%, offering a promising solution for a successful early diagnose and staging of DR in an automated fashion

A Novel Framework for Accurate and Non-Invasive Pulmonary Nodule Diagnosis by Integrating Texture and Contour Descriptors

An accurate computer aided diagnostic (CAD) system is very significant and critical for early detection of lung cancer. A new framework for lung nodule classification is proposed in this paper using different imaging markers from one computed tomography (CT) scan. Texture and shape features are combined together to show the main discriminative characteristics between malignant and benign pulmonary nodules. 7th-Order Markov Gibbs random field, (MGRF), is implemented to give a good description of the nodule’s appearance by involving the spatial data. A Various-views Marginal Aggregation Curvature Scale Space (MACSS) and the primitive geometrical properties are used to indicate the nodule’s shape complexity. Eventually, all these modeled descriptors are combined using a stacked autoencoder and softmax classifier to give the final diagnosis. Our system has been validated using 727 samples from the Lung Image Database Consortium. Our diagnosis framework’s accuracy, sensitivity, and specificity were 94.63%, 93.86%, 94.78% respectively, showing that our system serves as an important clinical assistive tool

Novel Approaches For Segmenting Cerebral Vasculature

In this chapter, we propose two segmentation approaches that are able to segment cerebral vasculature automatically and accurately. This would potentially help experts in the early analysis and diagnosis of severe diseases, specifically, multiple sclerosis

Radiomic-based framework for early diagnosis of lung cancer

© 2019 IEEE. This paper proposes a new framework for pulmonary nodule diagnosis using radiomic features extracted from a single computed tomography (CT) scan. The proposed framework integrates appearance and shape features to get a precise diagnosis for the extracted lung nodules. The appearance features are modeled using 3D Histogram of Oriented Gradient (HOG) and higher-order Markov Gibbs random field (MGRF) model because of their ability to describe the spatial non-uniformity in the texture of the nodule regardless of its size. The shape features are modeled using Spherical Harmonic expansion and some basic geometric features in order to have a full description of the shape complexity of the nodules. Finally, all the modeled features are fused and fed to a stacked autoencoder to differentiate between the malignant and benign nodules. Our framework is evaluated using 727 nodules which are selected from the Lung Image Database Consortium (LIDC) dataset, and achieved classification accuracy, sensitivity, and specificity of 93.12%, 92.47%, and 93.60% respectively

Early Diagnosis System For Lung Nodules Based On The Integration Of A Higher-Order Mgrf Appearance Feature Model And 3d-Cnn

In this chapter, a new system for lung nodule diagnosis, using features extracted from one computed tomography (CT) scan, is presented. To get an accurate diagnosis of the detected lung nodules, the proposed framework integrates the following two groups of features: (i) appearance features that are modeled using higher-order Markov–Gibbs random field (MGRF)-model that has the ability to describe the spatial inhomogeneities inside the lung nodule; and (ii) local features that are extracted using 3D convolutional neural networks (3D-CNN) because of its ability to exploit the spatial correlation of input data in an efficient way. The novelty of this chapter is to accurately model the appearance of the detected lung nodules using a new developed 7th-order MGRF model that has the ability to model the existing spatial inhomogeneities for both small and large detected lung nodules, in addition to the integration with the extracted local features from 3D-CNN. Finally, a deep autoencoder (AE) classifier is fed by the above two feature groups to distinguish between the malignant and benign nodules

A Comprehensive Framework for Accurate Classification of Pulmonary Nodules

© 2020 IEEE. A precise computerized lung nodule diagnosis framework is very important for helping radiologists to diagnose lung nodules at an early stage. In this manuscript, a novel system for pulmonary nodule diagnosis, utilizing features extracted from single computed tomography (CT) scans, is proposed. This system combines robust descriptors for both texture and contour features to give a prediction of the nodule\u27s growth rate, which is the standard clinical information for pulmonary nodules diagnosis. Spherical Sector Isosurfaces Histogram of Oriented Gradient is developed to describe the nodule\u27s texture, taking spatial information into account. A Multi-views Peripheral Sum Curvature Scale Space is used to demonstrate the nodule\u27s contour complexity. Finally, the two modeled features are augmented together utilizing a deep neural network to diagnose the nodules malignancy. For the validation purpose, the proposed system utilized 727 nodules from the Lung Image Database Consortium. The proposed system classification accuracy was 94.50%

Analysis of the Importance of Systolic Blood Pressure Versus Diastolic Blood Pressure in Diagnosing Hypertension: MRA Study.

© 2020 IEEE. Hypertension is one of the severest and most common diseases nowadays. It is considered one of the leading contributors to death worldwide. Specialists tend to diagnose hypertension taking into consideration both systolic and diastolic blood pressure (BP) measurements. However, some clinical hypothesis states that under 50 years of age, diastolic may be slightly more predictive of adverse events, while above that age, systolic may be more predictive. The question is should we give more value to systolic BP or diastolic BP when diagnosing diseases such as hypertension? Three different experiments were conducted in this study using magnetic resonance angiography (MRA) data to investigate this question. In each of these experiments, the following methodology was followed: 1) preprocess MRA data to remove noise, bias, or inhomogeneities, 2) segment the cerebral vasculature for each subject using a CNN-based approach, 3) extract vascular features that represent cerebral alterations that precede and accompany the development of hypertension, and 4) finally build feature vectors and classify data into either normotensives or hypertensives based on the cerebral alterations and the blood pressure measurements. The first experiment was conducted on original data set of 342 subjects. While the second and third experiments enlarged the original data set by generating more synthetic samples to make original data set large enough and balanced. Experimental results showed that systolic blood pressure might be more predictive than diastolic blood pressure in diagnosing hypertension with a classification accuracy of 89.3%

A CAD System for the Early Prediction of Hypertension based on Changes in Cerebral Vasculature

© 2019 IEEE. Hypertension is a leading cause for mortality in the US and a significant contributor to many vascular and non vascular diseases. Previous literature reports suggest that specific cerebral vascular alterations precede the onset of hypertension. In this manuscript, we propose a magnetic resonance angiography (MRA)-based computer-aided-diagnosis (CAD) system for the early detection of hypertension. The steps of the proposed CAD system are: 1) preprocessing of the MRA input data to correct the bias resulting from the magnetic field, remove noise effects, reduce contrast non-uniformities, enhance homogeneity using a generalized Gauss-Markov random field (GGMRF), and normalize data to enhance the segmentation process, 2) delineating the cerebral vasculature using a deep 3-D convolutional neural network (CNN) automatically and accurately, 3) extraction of vascular features (cerebrovascular diameters and tortuosity) that are reported to change with the progression of hypertension and constructing the feature vectors, 4) using the feature vectors for classifying input data using a support vector machine (SVM) classifier. We report a 90% classification accuracy in distinguishing between normal and potential hypertensive subjects. These results demonstrate the efficacy of using the proposed vascular features to predict pre-hypertension or hypertension. Clinicians could track the alterations of these vascular features over time for people at risk of developing hypertension for optimal medical management and mitigate adverse events

Segmentation of Infant Brain Using Nonnegative Matrix Factorization

This study develops an atlas-based automated framework for segmenting infants\u27 brains from magnetic resonance imaging (MRI). For the accurate segmentation of different structures of an infant\u27s brain at the isointense age (6-12 months), our framework integrates features of diffusion tensor imaging (DTI) (e.g., the fractional anisotropy (FA)). A brain diffusion tensor (DT) image and its region map are considered samples of a Markov-Gibbs random field (MGRF) that jointly models visual appearance, shape, and spatial homogeneity of a goal structure. The visual appearance is modeled with an empirical distribution of the probability of the DTI features, fused by their nonnegative matrix factorization (NMF) and allocation to data clusters. Projecting an initial high-dimensional feature space onto a low-dimensional space of the significant fused features with the NMF allows for better separation of the goal structure and its background. The cluster centers in the latter space are determined at the training stage by the K-means clustering. In order to adapt to large infant brain inhomogeneities and segment the brain images more accurately, appearance descriptors of both the first-order and second-order are taken into account in the fused NMF feature space. Additionally, a second-order MGRF model is used to describe the appearance based on the voxel intensities and their pairwise spatial dependencies. An adaptive shape prior that is spatially variant is constructed from a training set of co-aligned images, forming an atlas database. Moreover, the spatial homogeneity of the shape is described with a spatially uniform 3D MGRF of the second-order for region labels. In vivo experiments on nine infant datasets showed promising results in terms of the accuracy, which was computed using three metrics: the 95-percentile modified Hausdorff distance (MHD), the Dice similarity coefficient (DSC), and the absolute volume difference (AVD). Both the quantitative and visual assessments confirm that integrating the proposed NMF-fused DTI feature and intensity MGRF models of visual appearance, the adaptive shape prior, and the shape homogeneity MGRF model is promising in segmenting the infant brain DTI