Diabetic retinopathy (DR) is a serious retinal disease and is considered as a leading cause of blindness in the world. Ophthalmologists use optical coherence tomography (OCT) and fundus photography ...for the purpose of assessing the retinal thickness, and structure, in addition to detecting edema, hemorrhage, and scars. Deep learning models are mainly used to analyze OCT or fundus images, extract unique features for each stage of DR and therefore classify images and stage the disease. Throughout this paper, a deep Convolutional Neural Network (CNN) with 18 convolutional layers and 3 fully connected layers is proposed to analyze fundus images and automatically distinguish between controls (i.e. no DR), moderate DR (i.e. a combination of mild and moderate Non Proliferative DR (NPDR)) and severe DR (i.e. a group of severe NPDR, and Proliferative DR (PDR)) with a validation accuracy of 88%-89%, a sensitivity of 87%-89%, a specificity of 94%-95%, and a Quadratic Weighted Kappa Score of 0.91-0.92 when both 5-fold, and 10-fold cross validation methods were used respectively. A prior pre-processing stage was deployed where image resizing and a class-specific data augmentation were used. The proposed approach is considerably accurate in objectively diagnosing and grading diabetic retinopathy, which obviates the need for a retina specialist and expands access to retinal care. This technology enables both early diagnosis and objective tracking of disease progression which may help optimize medical therapy to minimize vision loss.
Food waste has become a major problem in this era and is considered a waste of resources. While food waste is increasing, the energy demand is increasing as well. Producing energy from food waste can ...be a suitable solution for the two problems. Food waste is rich in nutrients and organic compounds. This composition makes food waste suitable to produce hydrogen. Hydrogen has attracted the attention of researchers these days because of its high ability to produce energy with no side products other than water. Food waste can be converted to hydrogen through a thermochemical process called gasification. Gasification converts biomass into a mixture of combustible gases via partial oxidation under high temperatures. In this review, different types of food waste gasification are explored. The results from recent studies are summarized in tables and compared based on the hydrogen yield and process variables. Incrementing temperature and residence time favored hydrogen production along with decrementing feed concentration. Moreover, the usage of a suitable catalyst has significantly enhanced the hydrogen production. The results have shown that food waste gasification has a promising ability to produce hydrogen, however more studies are required to investigate more on the economic feasibility of the process before commercializing it.
•The potential of hydrogen production from Food Waste gasification is studied.•Studies of different food waste gasification types are investigated to demonstrate the influence of process variables.•Influence of temperature, residence time, feedstock concentration and catalyst type on hydrogen yield has been investigated.
Microalgal biofuel shows a promising potential for replacing nonrenewable fuel sources such as fossil fuels, as a response to the world's energy, economic, and environmental crisis. Although ...microalgae have many merits, the low economic viability of its biofuel production process requires collaborative interdisciplinary research to pave the way for commercializing this green fuel alternative. This paper reviews and evaluates the current status of microalgal biofuel production processes and presents a richer understanding of the available production pathways by comparing between various cultivation modes and systems, harvesting methods, lipid extraction methods, and conversion methods, in terms of advantages, disadvantages, feasibility, among other aspects. This study adds to the existing literature by shedding light on the non-destructive lipid extraction method (milking) and comparing it with the conventional extraction process. The results suggest that the milking method is superior to the conventional process because it eliminates harvesting costs, minimizes recultivation, decreases nutrient supply, and other advantages that are mentioned in this paper. Nevertheless, the development of this process requires better understanding of its mechanism and the microalgae strains which are suitable for it. Additionally, this study recommends integrating the biorefinery concept into the production of microalgal biofuel, implementing genetic engineering to tailor microalgae strains for the milking process, and utilizing artificial intelligence and process automation to speed up the technology development process.
•Conventional microalgal biofuel production methods are investigated.•Milking pathway proves supremacy over conventional pathways for many advantages.•The most energy-consuming stage is eliminated using the milking technique.•Recent progress regarding strains and equipment suitable for milking is discussed.
Oil leaks onto water surfaces from big tankers, ships, and pipeline cracks cause considerable damage and harm to the marine environment. Synthetic Aperture Radar (SAR) images provide an approximate ...representation for target scenes, including sea and land surfaces, ships, oil spills, and look-alikes. Detection and segmentation of oil spills from SAR images are crucial to aid in leak cleanups and protecting the environment. This paper introduces a two-stage deep-learning framework for the identification of oil spill occurrences based on a highly unbalanced dataset. The first stage classifies patches based on the percentage of oil spill pixels using a novel 23-layer Convolutional Neural Network. In contrast, the second stage performs semantic segmentation using a five-stage U-Net structure. The generalized Dice loss is minimized to account for the reduced oil spill representation in the patches. The results of this study are very promising and provide a comparable improved precision and Dice score compared to related work.
Abstract
Liver cancer is a major cause of morbidity and mortality in the world. The primary goals of this manuscript are the identification of novel imaging markers (morphological, functional, and ...anatomical/textural), and development of a computer-aided diagnostic (CAD) system to accurately detect and grade liver tumors non-invasively. A total of 95 patients with liver tumors (M = 65, F = 30, age range = 34–82 years) were enrolled in the study after consents were obtained. 38 patients had benign tumors (LR1 = 19 and LR2 = 19), 19 patients had intermediate tumors (LR3), and 38 patients had hepatocellular carcinoma (HCC) malignant tumors (LR4 = 19 and LR5 = 19). A multi-phase contrast-enhanced magnetic resonance imaging (CE-MRI) was collected to extract the imaging markers. A comprehensive CAD system was developed, which includes the following main steps:
i)
estimation of morphological markers using a new parametric spherical harmonic model,
ii)
estimation of textural markers using a novel rotation invariant gray-level co-occurrence matrix (GLCM) and gray-level run-length matrix (GLRLM) models, and
iii)
calculation of the functional markers by estimating the wash-in/wash-out slopes, which enable quantification of the enhancement characteristics across different CE-MR phases. These markers were subsequently processed using a two-stages random forest-based classifier to classify the liver tumor as benign, intermediate, or malignant and determine the corresponding grade (LR1, LR2, LR3, LR4, or LR5). The overall CAD system using all the identified imaging markers achieved a sensitivity of 91.8%±0.9%, specificity of 91.2%±1.9%, and F
$$_{1}$$
1
score of 0.91±0.01, using the leave-one-subject-out (LOSO) cross-validation approach. Importantly, the CAD system achieved overall accuracies of
$$88\%\pm 5\%$$
88
%
±
5
%
, 85%±2%, 78%±3%, 83%±4%, and 79%±3% in grading liver tumors into LR1, LR2, LR3, LR4, and LR5, respectively. In addition to LOSO, the developed CAD system was tested using randomly stratified 10-fold and 5-fold cross-validation approaches. Alternative classification algorithms, including support vector machine, naive Bayes classifier, k-nearest neighbors, and linear discriminant analysis all produced inferior results compared to the proposed two stage random forest classification model. These experiments demonstrate the feasibility of the proposed CAD system as a novel tool to objectively assess liver tumors based on the new comprehensive imaging markers. The identified imaging markers and CAD system can be used as a non-invasive diagnostic tool for early and accurate detection and grading of liver cancer.
Pressure-driven Molecular Dynamics simulations were employed to examine reverse osmosis desalination through graphene-oxide-based multilayered membranes. The effects of functionalization of the ...graphene-oxide flakes with poly(ethylene imine) branches in water permeability and salt rejection were described in detail. The role of the degree of structural rigidity of the membranes was also explored. A lower degree of rigidity of the membrane resulted in a 6–9 % increase in water permeability depending on the state of functionalization of the flakes. At constant membrane rigidity, functionalization of the membranes’ flakes led to approximately 30 % reduction in water permeability, but the water flux remained 2–3 orders of magnitude higher than that of conventional reverse-osmosis membranes. Moreover, functionalization of the membranes’ flakes resulted in a higher than 20 % enhancement in salt rejection at a pressure difference similar to that in actual reverse osmosis processes. Examination of the swelling behavior of the membranes showed that those based on the functionalized flakes exhibit a tendency to remain structurally coherent with an interlayer separation determined by the presence of the polymer branches. Description of the microscopic mechanisms related to the membranes’ water and ion flux, such as hydrogen bonding and concentration polarization, allowed the assessment of the contribution of different factors involved in desalination, providing new insight towards the fabrication of membranes with improved performance.
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•Introduction of a moderate structural flexibility of the membrane results in an ∼10 % increase in water permeability.•Functionalization of the graphene oxide flakes with branched poly(ethylene imine) enhances salt rejection by more than 20 %.•The degree of h-bonding between water and membrane depends strongly on the capacity of the flakes to act as h-bonding donors.•The net charge of the GO flakes induces concentration polarization and increases salt rejection upon increase of pressure.•A non-linear dependence of the salt rejection on the applied pressure difference is observed.
This study presents a non-invasive, automated, clinical diagnostic system for early diagnosis of lung cancer that integrates imaging data from a single computed tomography scan and breath bio-markers ...obtained from a single exhaled breath to quickly and accurately classify lung nodules. CT imaging and breath volatile organic compounds data were collected from 47 patients. Spherical Harmonics-based shape features to quantify the shape complexity of the pulmonary nodules, 7th-Order Markov Gibbs Random Field based appearance model to describe the spatial non-homogeneities in the pulmonary nodule, and volumetric features (size) of pulmonary nodules were calculated from CT images. 27 VOCs in exhaled breath were captured by a micro-reactor approach and quantied using mass spectrometry. CT and breath markers were input into a deep-learning autoencoder classifier with a leave-one-subject-out cross validation for nodule classification. To mitigate the limitation of a small sample size and validate the methodology for individual markers, retrospective CT scans from 467 patients with 727 pulmonary nodules, and breath samples from 504 patients were analyzed. The CAD system achieved 97.8% accuracy, 97.3% sensitivity, 100% specificity, and 99.1% area under curve in classifying pulmonary nodules.
The Abraham solvation parameter model, known alternatively as the Linear Solvation-Energy Relationships (LSER) model, is critically examined for its capacity to predict the hydration free-energy for ...a variety of solutes. The very linearity of the LSER approach is reconsidered as regards the hydrogen-bonding contribution to solvation free energy. This is done by combining the equation-of-state solvation thermodynamics with the statistical thermodynamics of hydrogen bonding. Thus, this hydrogen-bonding contribution is placed on a firm thermodynamic basis and the predictive calculations are now possible with known acidity and basicity, A and B, molecular descriptors. The LFER coefficients are now expressed in terms of the A and B descriptors. The methodology for the derivation of the new linear equations for the hydrogen-bonding contribution to solvation free energy is presented along with examples of calculations. The implication for the exchange of information on intermolecular interactions between diverse Quantitative Structure–Property Relationships (QSPR) and other approaches is discussed. The proposed changes and descriptor adjustments augments the LSER capacity for solvent screening and use in numerous applications in the broader chemical, biochemical and environmental sector. A critical discussion of perspectives and the challenging issues is also presented.
This study proposes a Computer-Aided Diagnostic (CAD) system to diagnose subjects with autism spectrum disorder (ASD). The CAD system identifies morphological anomalies within the brain regions of ...ASD subjects. Cortical features are scored according to their contribution in diagnosing a subject to be ASD or typically developed (TD) based on a trained machine-learning (ML) model. This approach opens the hope for developing a new CAD system for early personalized diagnosis of ASD. We propose a framework to extract the cerebral cortex from structural MRI as well as identifying the altered areas in the cerebral cortex. This framework consists of the following five main steps: (i) extraction of cerebral cortex from structural MRI; (ii) cortical parcellation to a standard atlas; (iii) identifying ASD associated cortical markers; (iv) adjusting feature values according to sex and age; (v) building tailored neuro-atlases to identify ASD; and (vi) artificial neural networks (NN) are trained to classify ASD. The system is tested on the Autism Brain Imaging Data Exchange (ABIDE I) sites achieving an average balanced accuracy score of 97±2%. This paper demonstrates the ability to develop an objective CAD system using structure MRI and tailored neuro-atlases describing specific developmental patterns of the brain in autism.
Recently, studies for non-invasive renal transplant evaluation have been explored to control allograft rejection. In this paper, a computer-aided diagnostic system has been developed to accommodate ...with an early-stage renal transplant status assessment, called RT-CAD. Our model of this system integrated multiple sources for a more accurate diagnosis: two image-based sources and two clinical-based sources. The image-based sources included apparent diffusion coefficients (ADCs) and the amount of deoxygenated hemoglobin (\mathrm{R}2^{*}). More specifically, these ADCs were extracted from 47 diffusion weighted magnetic resonance imaging (DW-MRI) scans at 11 different b-values (b0, b50, b100, ..., b1000 s/mm 2 ), while the \mathrm{R}2^{*} values were extracted from 30 blood oxygen leveldependent MRI (BOLD-MRI) scans at 5 different echo times (2ms,7ms, 12ms, 17ms, and 22ms). The clinical sources included serum creatinine (SCr) and creatinine clearance (CrCl). First, the kidney was segmented through the RT-CAD system using a geometric deformable model called a level-set method. Second, both ADCs and \mathrm{R}2^{*} were estimated for common patients (N=30) and then were integrated with the corresponding SCr and CrCl. Last, these integrated biomarkers were considered the discriminatory features to be used as trainers and testers for future deep learning-based classifiers such as stacked auto-encoders (SAEs). We used a k-fold cross-validation criteria to evaluate the RT-CAD system diagnostic performance, which achieved the following scores: 93.3%, 90.0%, and 95.0% in terms of accuracy, sensitivity, and specificity in differentiating between acute renal rejection (AR) and non-rejection (NR). The reliability and completeness of the RT-CAD system was further accepted by the area under the curve score of 0.92. The conclusions ensured that the presented RT-CAD system has a high reliability to diagnose the status of the renal transplant in a non-invasive way.
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