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Articles 1 - 4 of 4
Full-Text Articles in Medicine and Health Sciences
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 …
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 …
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” …