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Articles 1 - 4 of 4
Full-Text Articles in Analytical, Diagnostic and Therapeutic Techniques and Equipment
Clinical Diagnosis Support With Convolutional Neural Network By Transfer Learning, Spencer Fogleman, Jeremy Otsap, Sangrae Cho
Clinical Diagnosis Support With Convolutional Neural Network By Transfer Learning, Spencer Fogleman, Jeremy Otsap, Sangrae Cho
SMU Data Science Review
Breast cancer is prevalent among women in the United States. Breast cancer screening is standard but requires a radiologist to review screening images to make a diagnosis. Diagnosis through the traditional screening method of mammography currently has an accuracy of about 78% for women of all ages and demographics. A more recent and precise technique called Digital Breast Tomosynthesis (DBT) has shown to be more promising but is less well studied. A machine learning model trained on DBT images has the potential to increase the success of identifying breast cancer and reduce the time it takes to diagnose a patient, …
Machine Learning Approach To Distinguish Ulcerative Colitis And Crohn’S Disease Using Smote (Synthetic Minority Oversampling Technique) Methods, Kris Ghimire, Walter Lai, Yasser Omar, Thad Schwebke, Jamie Vo
Machine Learning Approach To Distinguish Ulcerative Colitis And Crohn’S Disease Using Smote (Synthetic Minority Oversampling Technique) Methods, Kris Ghimire, Walter Lai, Yasser Omar, Thad Schwebke, Jamie Vo
SMU Data Science Review
Irritable Bowel Disease (IBD) affects a sizable portion of the US population, causing symptoms such as vomiting, abdominal pain, and diarrhea. Despite the disease’s prevalence, the precise cause is not fully understood. This study consists of endoscopic and histological data from patients diagnosed with IBD and a control population for reference. The machine learning models' focus is to classify patients into IBD types. Several models were analyzed, including decision trees, logistic regression, and k-nearest neighbors. In addition, various methods of SMOTE were applied to determine the most effective transformation and ensuring that the dataset is balanced. The best model with …
Rule Out Screening For Undiagnosed Dementia And Alzheimer’S Disease Using An Ehr Based Machine Learning Solution, Branum Stephan, David A. Julovich, Dustin Bracy, Jeff Nguyen
Rule Out Screening For Undiagnosed Dementia And Alzheimer’S Disease Using An Ehr Based Machine Learning Solution, Branum Stephan, David A. Julovich, Dustin Bracy, Jeff Nguyen
SMU Data Science Review
Abstract. Current detection methods for Dementia and Alzheimer’s disease include cerebral spinal fluid (CSF) markers and/or the use of positron emission tomography (PET) imaging, both being high-cost, highly invasive testing methods. The need for low-cost, minimally invasive methods to prescreen individuals for cognitive impairment has been a challenge for many years. Today’s costs associated with an annual screen for all adults 65 and above using current methods (CSF, PET) reach well beyond trillions of dollars per year. Motivated by the limited accessibly and high costs, an alternative tool presented within this paper demonstrates an effective rule out screening for Dementia …
Using Ai To Diagnose Covid-19 From Patient Chest Ct Scans, Samuel Arellano, Liang Huang, Joe Jiang, Kenneth Richardson, Omar Yasser
Using Ai To Diagnose Covid-19 From Patient Chest Ct Scans, Samuel Arellano, Liang Huang, Joe Jiang, Kenneth Richardson, Omar Yasser
SMU Data Science Review
Rapid and accurate detection of COVID-19 remains the best weapon to control and prevent the spread of this pandemic, at least before a vaccine or treatment is available. In this study, we trained our custom computer vision models to predict COVID-19 from patients’ CT scans. We trained one model using Google’s AutoML Vision platform and achieved comparable accuracy with previously reported models. We also trained several custom models using transfer learning by taking advantage of several well-unknown pre-trained computer vision models, including Resnet and Inception models. The models are fine-tuned with a relatively large dataset and their high accuracy should …