Feasibility of a deep learning‐based algorithm for automated detection and classification of nasal polyps and inverted papillomas on nasal endoscopic images

2021 ◽  
Author(s):  
Benton Girdler ◽  
Hyun Moon ◽  
Mi Rye Bae, ◽  
Sung Seok Ryu ◽  
Jihye Bae ◽  
...  
Keyword(s):  
Deep Learning ◽  
Nasal Polyps ◽  
Automated Detection ◽  
Endoscopic Images ◽  
Inverted Papillomas

Automated detection and classification of shoulder arthroplasty models using deep learning

2020 ◽  
Vol 49 (10) ◽  
pp. 1623-1632
Author(s):  
Paul H. Yi ◽  
Tae Kyung Kim ◽  
Jinchi Wei ◽  
Xinning Li ◽  
Gregory D. Hager ◽  
...  
Keyword(s):  
Deep Learning ◽  
Shoulder Arthroplasty ◽  
Automated Detection

scholarly journals Automated detection and classification of fundus diabetic retinopathy images using synergic deep learning model

2020 ◽  
Vol 133 ◽  
pp. 210-216 ◽  
Author(s):  
K. Shankar ◽  
Abdul Rahaman Wahab Sait ◽  
Deepak Gupta ◽  
S.K. Lakshmanaprabu ◽  
Ashish Khanna ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Learning Model ◽  
Automated Detection ◽  
Deep Learning Model

scholarly journals Deep learning pipeline for automated detection and classification of central canal, lateral recess and neural foraminal stenosis on lumbar spine MRI

2021 ◽  
Vol 1 ◽  
pp. 100240
Author(s):  
Jiong Hao Tan ◽  
Lei Zhu ◽  
Kaiyuan Yang ◽  
Hiroshi Yoshioka ◽  
Beng Chin Ooi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Lumbar Spine ◽  
Central Canal ◽  
Automated Detection ◽  
Foraminal Stenosis ◽  
Lateral Recess ◽  
Spine Mri

scholarly journals Automated detection and classification of the proximal humerus fracture by using deep learning algorithm

2018 ◽  
Vol 89 (4) ◽  
pp. 468-473 ◽  
Author(s):  
Seok Won Chung ◽  
Seung Seog Han ◽  
Ji Whan Lee ◽  
Kyung-Soo Oh ◽  
Na Ra Kim ◽  
...  
Keyword(s):  
Deep Learning ◽  
Proximal Humerus Fracture ◽  
Proximal Humerus ◽  
Learning Algorithm ◽  
Automated Detection ◽  
Humerus Fracture ◽  
Deep Learning Algorithm

scholarly journals Automated Detection and Classification of Oral Lesions Using Deep Learning for Early Detection of Oral Cancer

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 132677-132693 ◽  
Author(s):  
Roshan Alex Welikala ◽  
Paolo Remagnino ◽  
Jian Han Lim ◽  
Chee Seng Chan ◽  
Senthilmani Rajendran ◽  
...  
Keyword(s):  
Deep Learning ◽  
Oral Cancer ◽  
Early Detection ◽  
Automated Detection ◽  
Oral Lesions

scholarly journals Automated Detection and Classification of Defective and Abnormal Dies in Wafer Images

2020 ◽  
Vol 10 (10) ◽  
pp. 3423
Author(s):  
Hsiang-Chieh Chen
Keyword(s):  
Deep Learning ◽  
Template Matching ◽  
Automated Detection ◽  
Acoustic Tomography ◽  
Final Phase ◽  
Step Method ◽  
Standard Template ◽  
Effectiveness And Efficiency ◽  
Template Extraction

This article presents an automated vision-based algorithm for the die-scale inspection of wafer images captured using scanning acoustic tomography (SAT). This algorithm can find defective and abnormal die-scale patterns, and produce a wafer map to visualize the distribution of defects and anomalies on the wafer. The main procedures include standard template extraction, die detection through template matching, pattern candidate prediction through clustering, and pattern classification through deep learning. To conduct the template matching, we first introduce a two-step method to obtain a standard template from the original SAT image. Subsequently, a majority of the die patterns are detected through template matching. Thereafter, the columns and rows arranged from the detected dies are predicted using a clustering method; thus, an initial wafer map is produced. This map is composed of detected die patterns and predicted pattern candidates. In the final phase of the proposed algorithm, we implement a deep learning-based model to determine defective and abnormal patterns in the wafer map. The experimental results verified the effectiveness and efficiency of our proposed algorithm. In conclusion, the proposed method performs well in identifying defective and abnormal die patterns, and produces a wafer map that presents important information for solving wafer fabrication issues.

Deep Learning Model for Automated Detection and Classification of Central Canal, Lateral Recess, and Neural Foraminal Stenosis at Lumbar Spine MRI

Radiology ◽  
2021 ◽  
pp. 204289
Author(s):  
James Thomas Patrick Decourcy Hallinan ◽  
Lei Zhu ◽  
Kaiyuan Yang ◽  
Andrew Makmur ◽  
Diyaa Abdul Rauf Algazwi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Lumbar Spine ◽  
Learning Model ◽  
Central Canal ◽  
Automated Detection ◽  
Foraminal Stenosis ◽  
Lateral Recess ◽  
Spine Mri ◽  
Deep Learning Model
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