Enhanced Ai-Based Machine Learning Model for an Accurate Segmentation and Classification Methods

Phone Laser Scanner becomes the versatile sensor module that is premised on Lamp Identification and Spanning methodology and is used in a spectrum of uses. There are several prior editorials in the literary works that concentrate on the implementations or attributes of these processes; even so, evaluations of all those inventive computational techniques reported in the literature have not even been performed in the required thickness. At ToAT that finish, we examine and summarize the latest advances in Artificial Intelligence based machine learning data processing approaches such as extracting features, fragmentation, machine vision, and categorization. In this survey, we have reviewed total 48 papers based on an enhanced AI based machine learning model for accurate classification and segmentation methods. Here, we have reviewed the sections on segmentation and classification of images based on machine learning models

A multilevel paradigm for deep convolutional neural network features selection with an application to human gait recognition

Human gait recognition (HGR) shows high importance in the area of video surveillance due to remote access and security threats. HGR is a technique commonly used for the identification of human style in daily life. However, many typical situations like change of clothes condition and variation in view angles degrade the system performance. Lately, different machine learning (ML) techniques have been introduced for video surveillance which gives promising results among which deep learning (DL) shows best performance in complex scenarios. In this article, an integrated framework is proposed for HGR using deep neural network and fuzzy entropy controlled skewness (FEcS) approach. The proposed technique works in two phases: In the first phase, deep convolutional neural network (DCNN) features are extracted by pre-trained CNN models (VGG19 and AlexNet) and their information is mixed by parallel fusion approach. In the second phase, entropy and skewness vectors are calculated from fused feature vector (FV) to select best subsets of features by suggested FEcS approach. The best subsets of picked features are finally fed to multiple classifiers and finest one is chosen on the basis of accuracy value. The experiments were carried out on four well-known datasets, namely, AVAMVG gait, CASIA A, B and C. The achieved accuracy of each dataset was 99.8, 99.7, 93.3 and 92.2%, respectively. Therefore, the obtained overall recognition results lead to conclude that the proposed system is very promising

Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases

Cardiothoracic and pulmonary diseases are a significant cause of mortality and morbidity worldwide. The COVID-19 pandemic has highlighted the lack of access to clinical care, the overburdened medical system, and the potential of artificial intelligence (AI) in improving medicine. There are a variety of diseases affecting the cardiopulmonary system including lung cancers, heart disease, tuberculosis (TB), etc., in addition to COVID-19-related diseases. Screening, diagnosis, and management of cardiopulmonary diseases has become difficult owing to the limited availability of diagnostic tools and experts, particularly in resource-limited regions. Early screening, accurate diagnosis and staging of these diseases could play a crucial role in treatment and care, and potentially aid in reducing mortality. Radiographic imaging methods such as computed tomography (CT), chest X-rays (CXRs), and echo ultrasound (US) are widely used in screening and diagnosis. Research on using image-based AI and machine learning (ML) methods can help in rapid assessment, serve as surrogates for expert assessment, and reduce variability in human performance. In this Special Issue, “Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases”, we have highlighted exemplary primary research studies and literature reviews focusing on novel AI/ML methods and their application in image-based screening, diagnosis, and clinical management of cardiopulmonary diseases. We hope that these articles will help establish the advancements in AI

Chest X-ray Classification for the Detection of COVID-19 Using Deep Learning Techniques

Recent technological developments pave the path for deep learning-based techniques to be used in almost every domain of life. The precision of deep learning techniques make it possible for these to be used in the medical field for the classification and detection of various diseases. Recently, the coronavirus (COVID-19) pandemic has put a lot of pressure on the health system all around the world. The diagnosis of COVID-19 is possible by PCR testing and medical imagining. Since COVID-19 is highly contagious, diagnosis using chest X-ray is considered safe in various situations. In this study, a deep learning-based technique is proposed to classify COVID-19 infection from other non-COVID-19 infections. To classify COVID-19, three different pre-trained models named EfficientNetB1, NasNetMobile and MobileNetV2 are used. The augmented dataset is used for training deep learning models while two different training strategies have been used for classification. In this study, not only are the deep learning model fine-tuned but also the hyperparameters are fine-tuned, which significantly improves the performance of the fine-tuned deep learning models. Moreover, the classification head is regularized to improve the performance. For the evaluation of the proposed techniques, several performance parameters are used to gauge the performance. EfficientNetB1 with regularized classification head outperforms the other models. The proposed technique successfully classifies four classes that include COVID-19, viral pneumonia, lung opacity, and normal, with an accuracy of 96.13%. The proposed technique shows superiority in terms of accuracy when compared with recent techniques present in the literature

Intelligent Biosignal Processing in Wearable and Implantable Sensors

This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine

Systematic analysis of genomic copy number variations in inflammatory bowel diseases

Crohn's disease (CD) and ulcerative colitis (UC) are chronic inflammatory bowel diseases (IBD) which are characterized by excessive immune responses to gut microbial flora in genetically susceptible individuals. A comprehensive dissection of the genetic predisposition to IBD needs to assess the contribution of all types of genetic variants including copy number variations (CNVs) to disease risk. In the presented thesis, two related studies were conducted to interrogate the presence of disease-relevant CNVs; In one, SNP-GWAS data-set of a German UC cohort together with four other independent UC case control series were recruited to perform a multi-step genome-wide association analysis. Three CNVs enriched in UC patients were identified; a 15.8 kb deletion upstream of the genes ABCC4 and CLDN10 at 13q32.1, a 119 kb duplication at 7p22.1, overlapping RNF216, ZNF815, OCM and CCZ1 and a 134 kb large duplication upstream of the KCNK9 gene at 8q24.3. Most of these candidate genes have been functionally implicated in inflammatory processes. The probable effects of Del 13q32.1 on expression of the two nearby genes were examined in intestinal biopsies of a UC patient panel. In parallel, genomic DNA from peripheral blood as well as intestinal biopsies from nine monozygotic twin pairs discordant for IBD manifestations (4 CD, 5 UC) were recruited and compared for genome-wide CNVs by means of array-CGH, quantitative PCR and sequencing. Initial CNV calls were also contrasted with expression data of the affected genes in the corresponding twin samples. No consistent copy number differences were however revealed in the genomic DNA of discordant twins. The results implicated the potential contribution of germline structural variants to the risk of UC. Post-zygotic genomic CNVs, however, appear not to be the common cause of IBD-discordance in MZ twin

Endoscopic optical coherence tomography for clinical studies in the gastrointestinal tract

Thesis (Ph. D.)--Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.Cataloged from PDF version of thesis.Includes bibliographical references.Optical coherence tomography (OCT) performs micrometer-scale, cross-sectional and three dimensional imaging by measuring the echo time delay of backscattered light. OCT imaging is performed using low-coherence interferometry. With the development of Fourier domain detection techniques and fiber-optic based OCT endoscopes, high speed internal body imaging was enabled, which makes OCT suitable for clinical research in the human gastrointestinal (GI) tract. Endoscopic OCT imaging is challenging because fast and stable optical scanning must be implemented inside a small imaging probe to acquire useable volumetric information from internal human bodies. Although several studies have shown the use of endoscopic OCT in human gastrointestinal tracts as a real-time surveillance tool, the capability of OCT has not yet been fully explored in endoscopic applications and OCT is not well accepted as a standard imaging modality for GI clinics due to hardware limitations and lack of comprehensive clinical evidences. This thesis presents a number of clinical studies using endoscopic OCT that provide solutions to clinical problems in the GI tract supported by statistically significant results and the development of ultrahigh speed endoscopic OCT system that enables advanced OCT imaging applications. In collaboration with medical partners, the structural features in the diseased esophagus identified from OCT images are compared before and immediately after different ablative therapies, and features that predict the treatment response are investigated. Working in collaboration with industrial partners, an ultrahigh speed endoscopic OCT imaging system is constructed for clinical research in gastroenterology. Distally actuated imaging catheters are developed, enabling the visualization of the detailed three-dimensional (3D) structure in the gastrointestinal tract. Finally, clinical pilot studies are conducted and demonstrate the utility of the ultrahigh speed endoscopic OCT imaging for broader surveillance coverage, pathology detection, and dye-less contrast enhancement. The convergence of 3D spatial resolution, imaging speed, field of view, and minimally invasive access enabled by endoscopic OCT are unmatched by most other biomedical imaging techniques. Though still in its early stage of clinical validation, endoscopic OCT may have a profound impact on human healthcare and industrial inspection by enabling visualization and quantification of 3D microstructure in situ and in real time.by Tsung-Han Tsai.Ph.D

ENGINEERING HIGH-RESOLUTION EXPERIMENTAL AND COMPUTATIONAL PIPELINES TO CHARACTERIZE HUMAN GASTROINTESTINAL TISSUES IN HEALTH AND DISEASE

In recent decades, new high-resolution technologies have transformed how scientists study complex cellular processes and the mechanisms responsible for maintaining homeostasis and the emergence and progression of gastrointestinal (GI) disease. These advances have paved the way for the use of primary human cells in experimental models which together can mimic specific aspects of the GI tract such as compartmentalized stem-cell zones, gradients of growth factors, and shear stress from fluid flow. The work presented in this dissertation has focused on integrating high-resolution bioinformatics with novel experimental models of the GI epithelium systems to describe the complexity of human pathophysiology of the human small intestines, colon, and stomach in homeostasis and disease. Here, I used three novel microphysiological systems and developed four computational pipelines to describe comprehensive gene expression patterns of the GI epithelium in various states of health and disease. First, I used single cell RNAseq (scRNAseq) to establish the transcriptomic landscape of the entire epithelium of the small intestine and colon from three human donors, describing cell-type specific gene expression patterns in high resolution. Second, I used single cell and bulk RNAseq to model intestinal absorption of fatty acids and show that fatty acid oxidation is a critical regulator of the flux of long- and medium-chain fatty acids across the epithelium. Third, I use bulk RNAseq and a machine learning model to describe how inflammatory cytokines can regulate proliferation of intestinal stem cells in an experimental model of inflammatory hypoxia. Finally, I developed a high throughput platform that can associate phenotype to gene expression in clonal organoids, providing unprecedented resolution into the relationship between comprehensive gene expression patterns and their accompanying phenotypic effects. Through these studies, I have demonstrated how the integration of computational and experimental approaches can measurably advance our understanding of human GI physiology.Doctor of Philosoph

ATHENA Research Book

The ATHENA European University is an alliance of nine Higher Education Institutions with the mission of fostering excellence in research and innovation by facilitating international cooperation. The ATHENA acronym stands for Advanced Technologies in Higher Education Alliance. The partner institutions are from France, Germany, Greece, Italy, Lithuania, Portugal, and Slovenia: the University of Orléans, the University of Siegen, the Hellenic Mediterranean University, the Niccolò Cusano University, the Vilnius Gediminas Technical University, the Polytechnic Institute of Porto, and the University of Maribor. In 2022 institutions from Poland and Spain joined the alliance: the Maria Curie-Skłodowska University and the University of Vigo. This research book presents a selection of the ATHENA university partners' research activities. It incorporates peer-reviewed original articles, reprints and student contributions. The ATHENA Research Book provides a platform that promotes joint and interdisciplinary research projects of both advanced and early-career researchers