DeTraC: Transfer Learning of Class Decomposed Medical Images in Convolutional Neural Networks

Due to the high availability of large-scale annotated image datasets, paramount progress has been made in deep convolutional neural networks (CNNs) for image classification tasks. CNNs enable learning highly representative and hierarchical local image features directly from data. However, the availability of annotated data, especially in the medical imaging domain, remains the biggest challenge in the field. Transfer learning can provide a promising and effective solution by transferring knowledge from generic image recognition tasks to the medical image classification. However, due to irregularities in the dataset distribution, transfer learning usually fails to provide a robust solution. Class decomposition facilitates easier to learn class boundaries of a dataset, and consequently can deal with any irregularities in the data distribution. Motivated by this challenging problem, the paper presents Decompose, Transfer, and Compose (DeTraC) approach, a novel CNN architecture based on class decomposition to improve the performance of medical image classification using transfer learning and class decomposition approach. DeTraC enables learning at the subclass level that can be more separable with a prospect to faster convergence.We validated our proposed approach with three different cohorts of chest X-ray images, histological images of human colorectal cancer, and digital mammograms. We compared DeTraC with the state-of-the-art CNN models to demonstrate its high performance in terms of accuracy, sensitivity, and specificity

Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network

Chest X-ray is the first imaging technique that plays an important role in the diagnosis of COVID-19 disease. Due to the high availability of large-scale annotated image datasets, great success has been achieved using convolutional neural networks (CNN s) for image recognition and classification. However, due to the limited availability of annotated medical images, the classification of medical images remains the biggest challenge in medical diagnosis. Thanks to transfer learning, an effective mechanism that can provide a promising solution by transferring knowledge from generic object recognition tasks to domain-specific tasks. In this paper, we validate and a deep CNN, called Decompose, Transfer, and Compose (DeTraC), for the classification of COVID-19 chest X-ray images. DeTraC can deal with any irregularities in the image dataset by investigating its class boundaries using a class decomposition mechanism. The experimental results showed the capability of DeTraC in the detection of COVID-19 cases from a comprehensive image dataset collected from several hospitals around the world. High accuracy of 93.1% (with a sensitivity of 100%) was achieved by DeTraC in the detection of COVID-19 X-ray images from normal, and severe acute respiratory syndrome cases

Predator efficacy and attraction to herbivore- induced volatiles determine insect pest selection of inferior host plant

Unlike mammals, most invertebrates provide no direct parental care for their progeny, which makes a well-selected oviposition site crucial. However, little is known about the female evaluation of opportunities and threats during host selection. Leveraging the wide range of host plants used by the polyphagous pest, Spodoptera littoralis, we investigate oviposition choice between two plants of different nutritional quality. Females prefer to lay their eggs on the host plant, which has inferior larval development and more natural enemies but provides lower predation rates. On the superior host plant, a major predator shows more successful search behavior and is more attracted to herbivore-induced volatiles. Our findings show that predator efficacy and odor-guided attraction, rather than predator abundance, determine enemy free space. We postulate that predators’ behaviors contribute to the weak correlation between preference and performance during host plant selection in S. littoralis and in polyphagous insects in general

Macular Thickness Variations with Axial Length in Healthy Individuals: Review Article

Background: In the diagnosis and evaluation of many visual illnesses, such as macular edema, macular thickness is an important metric. Gender, age, ethnicity, refraction, and axial length all have an effect on the retinal macular thickness. Preoperative calculations of intraocular lens power during cataract surgery and myopia research necessitate precise and accurate measurements of axial eye length. Objective: To assess the macular thickness in eyes with no ocular pathology, which is important because it serves as a reference for the consequent diagnosis, and to assess the effectiveness of treatment for various macula-related illnesses.Conclusion: Thickness of macula is not the same in all eyes, as it expected to vary with change of eye axial length, which can guide and help in various ocular diseases

Strengthening of Concrete Beams Using FRP Composites

Finite element analysis (FEA) is used to predict the behavior of reinforced concrete beams strengthened with fiber reinforced polymer (FRP). To verify and measure the accuracy of the FEM model, the current model results were compared with both experimental and theoretical available results. Four beams were studied simulating the Horsetail Creek Bridge, Oregon, USA. The first one is a control beam with no strengthening fiber.The second beam is strengthened with carbon fiber reinforced polymer (CFRP) oriented along the length of the beam to reinforce the flexure behavior. The third beam is wrapped with glass fiber reinforced polymer(GFRP) laminates representing the shear beam. The fourth one is strengthened with CFRP and GFRP laminates representing the flexure-shear beam.The load-strain for concrete, steel and fiber as well were represented and compared. In addition, the load deflection curves and crack patterns were developed and represented. The results showed that the modeling process was accurate in simulating the tested beams. It was also clear that using FRP in strengthening reinforced concrete beams is an effective method in improving both shear and flexural behavior of the beams

4S-DT: Self-Supervised Super Sample Decomposition for Transfer Learning With Application to COVID-19 Detection

Due to the high availability of large-scale annotated image datasets, knowledge transfer from pretrained models showed outstanding performance in medical image classification. However, building a robust image classification model for datasets with data irregularity or imbalanced classes can be a very challenging task, especially in the medical imaging domain. In this article, we propose a novel deep convolutional neural network,which we called self-supervised super sample decomposition for transfer learning (4S-DT) model. The 4S-DT encourages a coarse-to-fine transfer learning from large-scale image recognition tasks to a specific chest X-ray image classification task using a generic self-supervised sample decomposition approach. Our main contribution is a novel self-supervised learning mechanism guided by a super sample decomposition of unlabeled chest X-ray images. 4S-DT helps in improving the robustness of knowledge transformation via a downstream learning strategy with a class decomposition (CD) layer to simplify the local structure of the data. The 4S-DT can deal with any irregularities in the image dataset by investigating its class boundaries using a downstream CD mechanism. We used 50 000 unlabeled chest X-ray images to achieve our coarse-to-fine transfer learning with an application to COVID-19 detection, as an exemplar. The 4S-DT has achieved a high accuracy of 99.8% on the larger of the two datasets used in the experimental study and an accuracy of 97.54% on the smaller dataset, which was enriched by augmented images, out of which all real COVID-19 cases were detected

AUQantO: Actionable Uncertainty Quantification Optimization in deep learning architectures for medical image classification

Deep learning algorithms have the potential to automate the examination of medical images obtained in clinical practice. Using digitized medical images, convolution neural networks (CNNs) have demonstrated their ability and promise to discriminate among different image classes. As an initial step towards explainability in clinical diagnosis, deep learning models must be exceedingly precise, offering a measure of uncertainty for their predictions. Such uncertainty-aware models can help medical professionals in detecting complicated and corrupted samples for re-annotation or exclusion. This paper proposes a new model and data-agnostic mechanism, called Actionable Uncertainty Quantification Optimization (AUQantO) to improve the performance of deep learning architectures for medical image classification. This is achieved by optimizing the hyperparameters of the proposed entropy-based and Monte Carlo (MC) dropout uncertainty quantification techniques escorted by single- and multi-objective optimization methods, abstaining from the classification of images with a high level of uncertainty. This helps in improving the overall accuracy and reliability of deep learning models. To support the above claim, AUQantO has been validated with four deep learning architectures on four medical image datasets and using various performance metric measures such as precision, recall, Area Under the Receiver Operating Characteristic (ROC) Curve score (AUC), and accuracy. The study demonstrated notable enhancements in deep learning performance, with average accuracy improvements of 1.76% and 2.02% for breast cancer histology and 5.67% and 4.24% for skin cancer datasets, utilizing two uncertainty quantification techniques, and AUQantO further improved accuracy by 1.41% and 1.31% for brain tumor and 4.73% and 1.83% for chest cancer datasets while allowing exclusion of images based on confidence levels

A Comparative Study on Developing the Hybrid-Electric Vehicle ‎Systems and its Future Expectation over the Conventional Engines Cars

The use of hybrid electric vehicles (HEVs) as an alternative to traditional petroleum-powered cars has risen due to climate change, air pollution, and fuel depletion. The transportation sector is the second largest energy-consuming sector that accounts for 30% of the world’s total delivered energy and about 60% of world oil demand. In 2008, the transportation sector accounted for about 22% of total world CO2 emissions. Within this sector, road vehicles dominate oil consumption and represent 81% of total transportation energy demand. This review discusses opportunities to reduce energy consumed and greenhouse gases in this sector and briefly discusses the Hybrid electric vehicles as a solution to improve fuel economy and reduce emissions. Also, the Classification of Hybrid Electric Vehicles, and the General architectures of hybrid electric vehicles and their subtypes have been discussed. Hybrid electric vehicle system components, system analysis, and fuel economy benefits are also explained. As the comparison results proved that the benefits of improved engine thermal efficiency outweigh the losses caused by longer energy transmission paths and showed that hybridization can improve fuel economy by about 24% in typical urban cycles. This study offers a thorough analysis of hybrid electric vehicles, including information on the designs, and energy management systems, created by different researchers. According to the thorough analysis, the current systems can execute HEVs rather effectively, but their dependability and autonomous systems remain not satisfactory. Several variables, difficulties, and issues related to the future generation of hybrid cars have been highlighted in this research

Effect of Battery Charging Rates for Electric Hybrid Vehicle on Fuel consumption and emissions behaviors in different road conditions: a comparative Study with Conventional Car

The transportation sector is a major source of worldwide carbon emissions and represents a significant contributor to air quality issues, particularly in metropolitan areas. To address the enormous carburization issues, the transportation sector must embrace low-emission vehicle technology. The team is presently developing a passenger electric hybrid car with the goal of reducing the environmental pollution. Hybrid electric vehicles (HEVs), which have a record of success in lowering hydrocarbon usage, stand as an intermediary technique between fully electric cars and internal combustion engines. In the present work, the conventional gasoline car has been tested on road at different trips condition. The gasoline fuel consumption as well as the SI engine emissions have been tested. A complete Hybrid electric system has been impeded instead of conventional driving gasoline engines and tested at a different charging rate of the battery. A comparison between the tested systems shows increased fuel efficiency as a key advantage of using HEVs technology. However, there are still unresolved issues about the system\u27s energy reliability. HEVs emit up to 21.0, 5.8, 9.0-, and 23.3-times lower NOx, UHC, CO, and particle number emissions than comparable gasoline vehicles. The development of after-treatment systems, enhanced engine management methods and the use of renewable fuels are emerging as research strategic prioritie