Deep Neural Network-based Anomaly Detection and Recognition in Endoscopic Images of Vocal Folds
碩士 === 元智大學 === 電機工程學系甲組 === 107 === With the great success of deep learning in computer vision in recent years, it has gradually been applied to many fields, and the paper mainly combines it with biomedicine. Using deep learning on images, the vocal fold medical images captured by the endoscopes ar...
| Main Authors: | , |
|---|---|
| Other Authors: | |
| Format: | Others |
| Language: | en_US |
| Published: |
2019
|
| Online Access: | http://ndltd.ncl.edu.tw/handle/mtmv4t |
| Summary: | 碩士 === 元智大學 === 電機工程學系甲組 === 107 === With the great success of deep learning in computer vision in recent years, it has gradually been applied to many fields, and the paper mainly combines it with biomedicine. Using deep learning on images, the vocal fold medical images captured by the endoscopes are analyzed to automatically detect the location of the vocal fold disorders and distinguish the classes. This technique will be able to assist doctors in the diagnosis of the disease. Different from the previous analysis of vocal fold medical images that needs manual process of the selection of proper images for input. Instead, we analyze each frame of the whole video automatically, and only retain the key frames for subsequent calculation. Doing so will not only reduce labor-intensive operations but also be practical for clinical applications. We propose a system with two stages. Stage 1 is responsible for extracting the ROI from each frame in the vocal fold endoscopy films and stage 2 mainly detects the location of the vocal fold disorders from the extracted ROI by our developed deep learning model. Experiments have shown that our system achieves 79% sensitivity and 96% specificity in recognition of healthy and unhealthy cases, and achieves an accuracy rate of approximately 80% in classification of 3-class disorders.
|
|---|