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Articles 1 - 8 of 8
Full-Text Articles in Analytical, Diagnostic and Therapeutic Techniques and Equipment
Classification Of Breast Cancer Histopathological Images Using Semi-Supervised Gans, Balaji Avvaru, Nibhrat Lohia, Sowmya Mani, Vijayasrikanth Kaniti
Classification Of Breast Cancer Histopathological Images Using Semi-Supervised Gans, Balaji Avvaru, Nibhrat Lohia, Sowmya Mani, Vijayasrikanth Kaniti
SMU Data Science Review
Breast cancer is diagnosed more frequently than skin cancer in women in the United States. Most breast cancer cases are diagnosed in women, while children and men are less likely to develop the disease. Various tissues in the breast grow uncontrollably, resulting in breast cancer. Different treatments analyze microscopic histopathology images for diagnosis that help accurately detect cancer cells. Deep learning is one of the evolving techniques to classify images where accuracy depends on the volume and quality of labeled images. This study used various pre-trained models to train the histopathological images and analyze these models to create a new …
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 …
Fall Detection: Threshold Analysis Of Wrist-Worn Motion Sensor Signals, Michael J. Wolfe, Jospeh Caguioa, Andy Nguyen, Jacquelyn Cheun Phd
Fall Detection: Threshold Analysis Of Wrist-Worn Motion Sensor Signals, Michael J. Wolfe, Jospeh Caguioa, Andy Nguyen, Jacquelyn Cheun Phd
SMU Data Science Review
In this paper, we present a detection algorithm that accurately differentiates the event of a person falling from normal Activities of Daily Living (ADL). Our algorithm processes signals recorded from accelerometers and gyroscopes built into wearable activity monitoring devices such as smart watches that are worn on an individual’s wrist. Existing algorithms are accurate but imprecise, and rely too much on inconveniently-placed sensors. We propose a pipeline that improves precision without sacrificing accuracy and ease of use. We present the use of a combination of threshold-based and machine learning-based approaches to develop a refined fall-detection algorithm that builds upon previous …
Predicting Premature Birth Risk With Cfrna, Jason Lin, Jonathan Marin, John Santerre
Predicting Premature Birth Risk With Cfrna, Jason Lin, Jonathan Marin, John Santerre
SMU Data Science Review
Identifying which genes are early indicators for preterm births using cell-free ribonucleic acid (cfRNA) from non-invasive blood tests provided by pregnant women can improve prenatal care. Currently, there are no medical tests for early detection of preterm birth risk in routine checkups for pregnant women. Recent studies have shown potential genes that can predict preterm birth. Machine learning techniques are utilized to see if the Area Under the Curve (AUC) can be improved upon when evaluating the prediction accuracy for chosen genes sequences and concentrations. Using cell-free RNA data from non-invasive blood tests in conjunction with machine learning, we improve …
Open Cycle: Forecasting Ovulation For Family Planning, Karen Clark, Mridul Jain, Araya Messa, Vinh Le, Eric C. Larson
Open Cycle: Forecasting Ovulation For Family Planning, Karen Clark, Mridul Jain, Araya Messa, Vinh Le, Eric C. Larson
SMU Data Science Review
Abstract: Forecasting the length and different phases of a woman’s menstrual cycle, especially ovulation, is an important aspect of family planning. Predicting fertility has many uses in family planning including avoiding pregnancy and assisting couples in becoming pregnant. Past methods have focused on monitoring basal body temperature (BBT), cervical mucus changes, and hormonal levels to determine fertility. While these methods can provide an accurate prediction of ovulation these tests can become expensive, time-consuming, and do not provide prediction until after ovulation has occurred. In this paper, we compare conventional fertility assessment that is based on a rule known as “three-over-six” …