A team of researchers from Ochsner Health recently published an insightful article in the International Forum of Allergy & Rhinology exploring the application of convolutional neural networks (CNNs) to improve the accuracy and efficiency of nasal endoscopy.
Ochsner Health SystemJun 28 2024 A team of researchers from Ochsner Health recently published an insightful article in the International Forum of Allergy & Rhinology exploring the application of convolutional neural networks to improve the accuracy and efficiency of nasal endoscopy. The study, authored by resident physician Dr. Vinayak Ganeshan under the guidance of senior otolaryngologist Dr. Edward D.
The researchers configured the YOLOv8 object detection model to perform three tasks: classify the presence of a turbinate, detect its location, and apply a segmentation mask delineating its borders. Transfer learning was employed to refine the model's performance on NE images through backpropagation and stochastic gradient descent. By manually selecting hyperparameters and halting training upon a 15-epoch stall in validation performance, the model achieved impressive results.
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