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Full-Text Articles in Life Sciences
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, …
Stationary Exercise Classification Using Imus And Deep Learning, Andrew M. Heroy, Zackary Gill, Samantha Sprague, David Stroud, John Santerre
Stationary Exercise Classification Using Imus And Deep Learning, Andrew M. Heroy, Zackary Gill, Samantha Sprague, David Stroud, John Santerre
SMU Data Science Review
In the current market, successful fitness tracking devices utilize heart rate and GPS to determine performance. These devices are useful, but don't extensively classify stationary exercise. This paper proposes a modern approach for tuning and investigating optimal neural network types on stationary exercises using Inertial Measurement Units (IMUs). Using three IMUs located on the ankle, waist, and wrist, data is collected to map the body as it moves during the stationary physical activity. A novel five-stage deep learning tuning system was written and deployed to classify user movement as one of three classes: air squats, jumping jacks, and kettlebell swings. …