Medicine and Health Sciences Commons^™

Efficient Framework For Brain Tumor Detection Using Different Deep Learning Techniques, Fatma Taher, Mohamed R. Shoaib, Heba M. Emara, Khaled M. Abdelwahab, Fathi E. Abd El-Samie, Mohammad T. Haweel

All Works

The brain tumor is an urgent malignancy caused by unregulated cell division. Tumors are classified using a biopsy, which is normally performed after the final brain surgery. Deep learning technology advancements have assisted the health professionals in medical imaging for the medical diagnosis of several symptoms. In this paper, transfer-learning-based models in addition to a Convolutional Neural Network (CNN) called BRAIN-TUMOR-net trained from scratch are introduced to classify brain magnetic resonance images into tumor or normal cases. A comparison between the pre-trained InceptionResNetv2, Inceptionv3, and ResNet50 models and the proposed BRAIN-TUMOR-net is introduced. The performance of the proposed model is …

Tutorial: Neuro-Symbolic Ai For Mental Healthcare, Kaushik Roy, Usha Lokala, Manas Gaur, Amit Sheth

Publications

Artificial Intelligence (AI) systems for mental healthcare (MHCare) have been ever-growing after realizing the importance of early interventions for patients with chronic mental health (MH) conditions. Social media (SocMedia) emerged as the go-to platform for supporting patients seeking MHCare. The creation of peer-support groups without social stigma has resulted in patients transitioning from clinical settings to SocMedia supported interactions for quick help. Researchers started exploring SocMedia content in search of cues that showcase correlation or causation between different MH conditions to design better interventional strategies. User-level Classification-based AI systems were designed to leverage diverse SocMedia data from various MH conditions, …

Cov-Inception: Covid-19 Detection Tool Using Chest X-Ray, Aswini Thota, Ololade Awodipe, Rashmi Patel

SMU Data Science Review

Since the pandemic started, researchers have been trying to find a way to detect COVID-19 which is a cost-effective, fast, and reliable way to keep the economy viable and running. This research details how chest X-ray radiography can be utilized to detect the infection. This can be for implementation in Airports, Schools, and places of business. Currently, Chest imaging is not a first-line test for COVID-19 due to low diagnostic accuracy and confounding with other viral pneumonia. Different pre-trained algorithms were fine-tuned and applied to the images to train the model and the best model obtained was fine-tuned InceptionV3 model …

Scai Shock Stage Classification: What Is Missing From The Latest Update?, Biykem Bozkurt

Journal of Shock and Hemodynamics

The revised Society for Cardiovascular Angiography and Interventions classifications reflect graduation of severity within each stage and pathway by which patients progress or recover. However, they are limited regarding the following: their predictive role to guide therapy; escalation of therapy or referral; variability in diagnostic criteria and interpretation; presence of other disease modifiers and confounders; variability of etiology and reversibility of cause; response to therapy and trajectory to be taken into risk stratification; magnitude and phenotypes of end-organ damage. Thus, we need a modified risk score to predict the necessity to escalate therapy and consider advanced therapies, such as mechanical …

Sel-Covidnet: An Intelligent Application For The Diagnosis Of Covid-19 From Chest X-Rays And Ct-Scans, Ahmad Al Smadi, Ahed Abugabah, Ahmad Mohammad Al-Smadi, Sultan Almotairi

All Works

COVID-19 detection from medical imaging is a difficult challenge that has piqued the interest of experts worldwide. Chest X-rays and computed tomography (CT) scanning are the essential imaging modalities for diagnosing COVID-19. All researchers focus their efforts on developing viable methods and rapid treatment procedures for this pandemic. Fast and accurate automated detection approaches have been devised to alleviate the need for medical professionals. Deep Learning (DL) technologies have successfully recognized COVID-19 situations. This paper proposes a developed set of nine deep learning models for diagnosing COVID-19 based on transfer learning and implementation in a novel architecture (SEL-COVIDNET). In which …

Prediction Of Electronic Nicotine Delivery Systems Use In Copdgene Using Multi-Omics Biomarkers, Andrew Gregory, Zhonghui Xu, Katherine Pratte, Seth Berman, Noah Lichtblau, Robin Lu, Robert Chase, Jeong Yun, Aabida Saferali, Edwin K. Silverman, Craig P. Hersh, Russell P. Bowler, Adel Boueiz, Peter J. Castaldi

Medical Student Research Symposium

Introduction: Biomarkers may be useful for understanding the toxic effects of vaping. Herein, we identified blood transcriptomic and proteomic biomarkers of vaping, related them to prospective health outcomes, and investigated their ability to accurately distinguish vapers from smokers.

Methods: We grouped 3,892 COPDGene study participants as vapers, current smokers, former smokers, or dual users. We tested for associations with 21,471 blood RNA transcripts and 4,979 plasma proteins. We related the significant biomarkers to 6.5 years of incident health events. To assess the discriminative performance of multi-omics for vaping, we constructed linear discriminant analysis models with cross-validation for RNA …

The Multidimensional Test Anxiety Scale: A Latent Profile Analysis And An Examination Of Measurement Invariance, Gabrielle Francis

USF Tampa Graduate Theses and Dissertations

Standardized testing is an integral part of the English and American education systems. The objectives of these tests are to evaluate students, teachers, and schools. However, this evaluation has unintended consequences, one of which is test anxiety. Over the last 50 years, there has been an increase in studies on test anxiety because of the widespread use of standardized tests (Hembree, 1988; von der Embse et al., 2019). However, two areas that have seen little attention are the measurement invariance of test anxiety scales across demographic groups, and the creation of classification standards for these test anxiety scales to increase …

The Classification Of Scoliosis Braces Developed By Sosort With Srs, Ispo, And Posna And Approved By Esprm., Stefano Negrini, Angelo Gabriele Aulisa, Pavel Cerny, Jean Claude De Mauroy, Jeb Mcaviney, Andrew Mills, Sabrina Donzelli, Theodoros B. Grivas, M Timothy Hresko, Tomasz Kotwicki, Hubert Labelle, Louise Marcotte, Martin Matthews, Joe O'Brien, Eric C. Parent, Nigel Price, Rigo Manuel, Luke Stikeleather, Michael G. Vitale, Man Sang Wong, Grant Wood, James Wynne, Fabio Zaina, Marco Brayda Bruno, Suncica Bulat Würsching, Yilgor Caglar, Patrick Cahill, Eugenio Dema, Patrick Knott, Andrea Lebel, Grigorii Lein, Peter O. Newton, Brian G. Smith

Manuscripts, Articles, Book Chapters and Other Papers

PURPOSE: Studies have shown that bracing is an effective treatment for patients with idiopathic scoliosis. According to the current classification, almost all braces fall in the thoracolumbosacral orthosis (TLSO) category. Consequently, the generalization of scientific results is either impossible or misleading. This study aims to produce a classification of the brace types.

METHODS: Four scientific societies (SOSORT, SRS, ISPO, and POSNA) invited all their members to be part of the study. Six level 1 experts developed the initial classifications. At a consensus meeting with 26 other experts and societies' officials, thematic analysis and general discussion allowed to define the classification …

A Machine Learning Framework For Identifying Molecular Biomarkers From Transcriptomic Cancer Data, Md Abdullah Al Mamun

FIU Electronic Theses and Dissertations

Cancer is a complex molecular process due to abnormal changes in the genome, such as mutation and copy number variation, and epigenetic aberrations such as dysregulations of long non-coding RNA (lncRNA). These abnormal changes are reflected in transcriptome by turning oncogenes on and tumor suppressor genes off, which are considered cancer biomarkers.

However, transcriptomic data is high dimensional, and finding the best subset of genes (features) related to causing cancer is computationally challenging and expensive. Thus, developing a feature selection framework to discover molecular biomarkers for cancer is critical.

Traditional approaches for biomarker discovery calculate the fold change for each …

Innovations In Cervical Spine Trauma: Developing The Next Generation Upper Cervical Spine Injury Classification System, Brian A Karamian, Hannah Levy, Paul D. Minetos, Michael L. Smith, Alex R. Vaccaro

Rothman Institute Faculty Papers

The upper cervical spine not only consists of intricate bony and ligamentous anatomy affording unique flexibility but also has increased susceptibility to injuries. The upper cervical spine trauma can result in a wide spectrum of injuries that can be managed both operatively and nonoperatively. Several existing classification systems have been proposed to describe injuries of the upper cervical spine, many of which rely on anatomic descriptions of injury location. Prior fracture classifications are limited in scope, characterizing fractures restricted to a single region of the upper cervical spine, and fail to provide insight into injury management. The AO Spine Upper …

Exploring The Concept Of The Digital Educator During Covid-19, Fernando Jimenez, Gracia Sanchez, Jose Palma, Luis Miralles-Pechuán, Juan A. Botia

Articles

T In many machine learning classification problems, datasets are usually of high dimensionality and therefore require efficient and effective methods for identifying the relative importance of their attributes, eliminating the redundant and irrelevant ones. Due to the huge size of the search space of the possible solutions, the attribute subset evaluation feature selection methods are not very suitable, so in these scenarios feature ranking methods are used. Most of the feature ranking methods described in the literature are univariate methods, which do not detect interactions between factors. In this paper, we propose two new multivariate feature ranking methods based on …

Smart Covid-3d-Scnn: A Novel Method To Classify X-Ray Images Of Covid-19, Ahed Abugabah, Atif Mehmood, Ahmad Ali Al Zubi, Louis Sanzogni

All Works

The outbreak of the novel coronavirus has spread worldwide, and millions of people are being infected. Image or detection classification is one of the first application areas of deep learning, which has a significant contribution to medical image analysis. In classification detection, one or more images (detection) are usually used as input, and diagnostic variables (such as whether there is a disease) are used as output. The novel coronavirus has spread across the world, infecting millions of people. Early-stage detection of critical cases of COVID-19 is essential. X-ray scans are used in clinical studies to diagnose COVID-19 and Pneumonia early. …

Validation Of The European Hernia Society Ventral Hernia Classification System, Jose Lopez-Vera, Erik Askenasy, Jacob Greenberg, Jerrod Keith, Robert Martindale, J Scott Roth, Zuhair Ali, Mike K. Liang

Gulf Coast Division Research Day 2022

No abstract provided.

Detection Of Correct Pregnancy Status In Lactating Dairy Cattle Using Mars Data Mining Algorithm, Demet Çanga, Mustafa Boğa

Turkish Journal of Veterinary & Animal Sciences

In this study, it is aimed to determine pregnancy outcomes by using multivariate adaptive regression splines (MARS) algorithm for classification type problems. For this purpose, data obtained from a private dairy farm in the Konya region of Türkiye in 2020 were used to determine pregnancy outcomes in Holstein dairy cattle. It has been determined how to perform statistical analyses on solving classification-type problems with the MARS algorithm and how to use R packages (caret and earth) by creating an R script file. After the analysis, the MARS estimation equation was created and in finding the probability of being pregnant: While …

Uncertainty Estimation In Classification Of Mgnt Using Radiogenomics For Glioblastoma Patients, W. Farzana, Z. A. Shboul, A. Temtam, K. M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

Glioblastoma Multiforme (GBM) is one of the most malignant brain tumors among all high-grade brain cancers. Temozolomide (TMZ) is the first-line chemotherapeutic regimen for glioblastoma patients. The methylation status of the O6-methylguanine-DNA-methyltransferase (MGMT) gene is a prognostic biomarker for tumor sensitivity to TMZ chemotherapy. However, the standardized procedure for assessing the methylation status of MGMT is an invasive surgical biopsy, and accuracy is susceptible to resection sample and heterogeneity of the tumor. Recently, radio-genomics which associates radiological image phenotype with genetic or molecular mutations has shown promise in the non-invasive assessment of radiotherapeutic treatment. This study proposes a machine-learning framework …

Using Electrooculography With Visual Stimulus Tracking Test In Diagnosing Of Adhd: Findings From Machine Learning Algorithms, Fatma Lati̇foğlu, Mustafa Yasi̇n Esas, Rami̇s İleri̇, Sevgi̇ Özmen, Esra Demi̇rci̇

Turkish Journal of Medical Sciences

Background/aim: Attention deficit hyperactivity disorder (ADHD), one of the most common neurodevelopmental disorders in childhood, is diagnosed clinically by assessing the symptoms of inattention, hyperactivity, and impulsivity. Also, there are limited objective assessment tools to support the diagnosis. Thus, in this study, a new electrooculography (EOG) based on visual stimulus tracking to support the diagnosis of ADHD was proposed. Materials and methods: Reference stimulus one-to-one tracking numbers (RSOT) and colour game detection (CGD) were applied to 53 medication-free children with ADHD and 36 healthy controls (HCs). Also, the test was applied six months after the treatment to children with ADHD. …