Browsing by Subject "Neural Networks"

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    Detecting Suboptimal Breast Positioning in Screening Mammograms Using Neural Networks
    (2024) Whipps, Zachary

    Proper breast positioning in screening mammography minimizes the likelihood that tissue is obscured or missing from a mammogram and decreases the rate of technical repeats and misdiagnosis. Several factors contribute to improper positioning including technologist experience, patient condition, and patient body habitus. One way to help the technologist identify and improve improper positioning is through MQSA’s required periodic image quality performance evaluations known as the EQUIP review. While valuable for performance improvement, the number of cases evaluated during these reviews is relatively small compared to the overall number of studies a technologist performs and may not provide the nuanced feedback needed for process improvement. In this study, we have developed convolutional neural network models to detect several aspects of suboptimal positioning as defined by the ACR’s positioning standards towards providing technologists valuable feedback for measuring performance improvement.The dataset contained a total of 600 clinical screening mammograms with a variety of positioning-related anomalies. An experienced technologist labeled relevant anomalies and image features important to breast positioning assessment. Image review tasks were separated into one of two categories: classification tasks and segmentation tasks. Using a neural network approach, segmentation models were developed to identify the pectoralis muscle boundary, skin folds, the inframammary fold (IMF), and nipple location. Classification models were developed to determine if the nipple was in profile, if the breast was droopy, and if the IMF was open, not open, or not shown. The classification models had a final accuracy of 89.3% for classifying whether the IMF was shown or not, 81.3% for determining if the IMF was shown and open, shown and not open, or not shown, and 80.2% for identifying if the nipple was in profile. These algorithms will ultimately be implemented to evaluate all screening mammograms acquired at Duke Health to automatically identify breast positioning features for clinical feedback.

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    Myoelectric Signal Classification Using A Finite Impulse Response Neural Network
    (1994) Englehart, K. B.; Hudgins, B. S.; Stevenson, M.; Parker, P. A.
    Recent work by Hudgins has proposed a neural network-based approach to classifying themyoelectric signal (MES) elicited at the onset of movement of the upper limb. A standard feed forward artificial network was trained (using the backpropagation algorithm) to discriminatearnongst four classes of upper-limb movements from the MES acquired from the biceps and triceps muscles The approach has demonstrated a powerful means of classifying limb function intent from the MES during natural muscular contraction, but the static nature of the network architecture fails to fullycharacterize the dynamic structure inherent in the MES. It has been demonstrated that a finite-impulseresponse (FIR) network has the ability to incorporate the temporal structure of a signal, representing the relationships between events in time and providing translation invariance of the relevant feature set. The application of this network architecture to limb function discrimination from the MES is described here.