Lesion Classification by Model-Based Feature Extraction: A Differential Affine Invariant Model of Soft Tissue Elasticity in CT Images.

The elasticity of soft tissues has been widely considered a characteristic property for differentiation of healthy and lesions and, therefore, motivated the development of several elasticity imaging modalities, for example, ultrasound elastography, magnetic resonance elastography, and optical coherence elastography to directly measure the tissue elasticity. This paper proposes an alternative approach of modeling the elasticity for prior knowledge-based extraction of tissue elastic characteristic features for machine learning (ML) lesion classification using computed tomography (CT) imaging modality. The model describes a dynamic non-rigid (or elastic) soft tissue deformation in differential manifold to mimic the tissues' elasticity under wave fluctuation in vivo.

Microservice Application Scheduling in Multi-Tiered Fog-Computing-Enabled IoT.

Fog computing extends mobile cloud computing facilities at the network edge, yielding low-latency application execution. To supplement cloud services, computationally intensive applications can be distributed on resource-constrained mobile devices by leveraging underutilized nearby resources to meet the latency and bandwidth requirements of application execution. Building upon this premise, it is necessary to investigate idle or underutilized resources that are present at the edge of the network.

Foreign Body Aspiration Mimicking an Endobronchial Neoplasm: A Case Report and Review of the Literature.

Foreign body aspiration (FBA) is infrequently encountered in the adult population, with major risk factors including advancing age, intoxication, and disorders of the central nervous system. Here, we present a case of FBA in an adult undergoing routine lung cancer screening to review imaging findings and highlight potential pitfalls for the practicing radiologist. A low-dose chest computed tomography (CT) scan was performed for lung cancer screening in a 57-year-old male with a one-month history of worsening dyspnea and cough.

Longitudinal Chest X-ray Scores and their Relations with Clinical Variables and Outcomes in COVID-19 Patients.

This study evaluated the temporal characteristics of lung chest X-ray (CXR) scores in COVID-19 patients during hospitalization and how they relate to other clinical variables and outcomes (alive or dead). This is a retrospective study of COVID-19 patients. CXR scores of disease severity were analyzed for: (i) survivors ( = 224) versus non-survivors ( = 28) in the general floor group, and (ii) survivors ( = 92) versus non-survivors ( = 56) in the invasive mechanical ventilation (IMV) group.

Cow dung putrefaction via vermicomposting using Eisenia fetida and its influence on seed sprouting and vegetative growth of Viola wittrockiana (pansy).

The current research was conducted at Vermi-tech Unit, Muzaffarabad in 2018 to evaluate the efficacy of cow dung and vermicompost on seed sprouting, seedlings, and vegetative developmental parameters of Viola x wittrokiana (pansy). In the current study, vermicompost was produced using Eisenia fetida. Physicochemical parameters of vermicompost and organic manure were recorded before each experimentation.

Vector textures derived from higher order derivative domains for classification of colorectal polyps.

Textures have become widely adopted as an essential tool for lesion detection and classification through analysis of the lesion heterogeneities. In this study, higher order derivative images are being employed to combat the challenge of the poor contrast across similar tissue types among certain imaging modalities. To make good use of the derivative information, a novel concept of vector texture is firstly introduced to construct and extract several types of polyp descriptors.

Classification of Non-Functional Requirements From IoT Oriented Healthcare Requirement Document.

Internet of Things (IoT) involves a set of devices that aids in achieving a smart environment. Healthcare systems, which are IoT-oriented, provide monitoring services of patients' data and help take immediate steps in an emergency. Currently, machine learning-based techniques are adopted to ensure security and other non-functional requirements in smart health care systems.

A dynamic lesion model for differentiation of malignant and benign pathologies.

Malignant lesions have a high tendency to invade their surrounding environment compared to benign ones. This paper proposes a dynamic lesion model and explores the 2nd order derivatives at each image voxel, which reflect the rate of change of image intensity, as a quantitative measure of the tendency. The 2nd order derivatives at each image voxel are usually represented by the Hessian matrix, but it is difficult to quantify a matrix field (or image) through the lesion space as a measure of the tendency.

Leveraging Deep Learning for Designing Healthcare Analytics Heuristic for Diagnostics.

Healthcare Informatics is a phenomenon being talked about from the early 21st century in the era in which we are living. With evolution of new computing technologies huge amount of data in healthcare is produced opening several research areas. Managing the massiveness of this data is required while extracting knowledge for decision making is the main concern of today.

Predicting COVID-19 Pneumonia Severity on Chest X-ray With Deep Learning.

Introduction The need to streamline patient management for coronavirus disease-19 (COVID-19) has become more pressing than ever. Chest X-rays (CXRs) provide a non-invasive (potentially bedside) tool to monitor the progression of the disease. In this study, we present a severity score prediction model for COVID-19 pneumonia for frontal chest X-ray images.

Deep transfer learning artificial intelligence accurately stages COVID-19 lung disease severity on portable chest radiographs.

This study employed deep-learning convolutional neural networks to stage lung disease severity of Coronavirus Disease 2019 (COVID-19) infection on portable chest x-ray (CXR) with radiologist score of disease severity as ground truth. This study consisted of 131 portable CXR from 84 COVID-19 patients (51M 55.1±14.

Multilayer feature selection method for polyp classification via computed tomographic colonography.

Polyp classification is a feature selection and clustering process. Picking the most effective features from multiple polyp descriptors without redundant information is a great challenge in this procedure. We propose a multilayer feature selection method to construct an optimized descriptor for polyp classification with a feature-grouping strategy in a hierarchical framework.

Multi-scale characterizations of colon polyps via computed tomographic colonography.

Texture features have played an essential role in the field of medical imaging for computer-aided diagnosis. The gray-level co-occurrence matrix (GLCM)-based texture descriptor has emerged to become one of the most successful feature sets for these applications. This study aims to increase the potential of these features by introducing multi-scale analysis into the construction of GLCM texture descriptor.

3D-GLCM CNN: A 3-Dimensional Gray-Level Co-Occurrence Matrix-Based CNN Model for Polyp Classification via CT Colonography.

Accurately classifying colorectal polyps, or differentiating malignant from benign ones, has a significant clinical impact on early detection and identifying optimal treatment of colorectal cancer. Convolution neural network (CNN) has shown great potential in recognizing different objects (e.g.

Invariant domain watermarking using heaviside function of order alpha and fractional Gaussian field.

Digital image watermarking is an important technique for the authentication of multimedia content and copyright protection. Conventional digital image watermarking techniques are often vulnerable to geometric distortions such as Rotation, Scaling, and Translation (RST). These distortions desynchronize the watermark information embedded in an image and thus disable watermark detection.