Computer Aided Diagnosis For Retinal Lesion Detection Based on Adaptive Boosting Classifier

2020 ◽  
Author(s):  
Wincy Jenefer ◽  
Karpagarajesh G ◽  
Sherlin Leela Princy ◽  
Helen Vedanayagi Anita ◽  
Martina M
Keyword(s):  
Computer Aided Diagnosis ◽  
Lesion Detection ◽  
Adaptive Boosting ◽  
Retinal Lesion ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals A Deep Learning-based Computer-aided Diagnosis System for Mammographic Lesion Detection

2018 ◽  
Vol 54 (8) ◽  
pp. 659-669 ◽  
Author(s):  
Shintaro SUZUKI ◽  
Xiaoyong ZHANG ◽  
Noriyasu HOMMA ◽  
Kei ICHIJI ◽  
Yumi TAKANE ◽  
...  
Keyword(s):  
Deep Learning ◽  
Computer Aided Diagnosis ◽  
Lesion Detection ◽  
Diagnosis System ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals Survey on Recent Works in Computed Tomography based Computer ‑ Aided Diagnosis of Liver using Deep Learning Techniques

2020 ◽  
Vol 5 (7) ◽  
pp. 173-181
Author(s):  
E. Emerson Nithiyaraj ◽  
S. Arivazhagan
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Imaging Modality ◽  
Computer Aided Diagnosis ◽  
Lesion Detection ◽  
Machine Learning Algorithms ◽  
Learning Technology ◽  
X Rays ◽  
Computer Aided ◽  
Aided Diagnosis

Computed tomography (CT) scanning is a non-invasive diagnostic imaging technique that provides more detailed information about the liver than standard X-rays. Unlike ultrasound (US) examination, the quality of the CT image is not highly operator dependent. Plenty of works has been done using computer-aided diagnosis (CAD) for liver using conventional machine learning algorithms with better results. Recent advances especially in deep learning technology, can detect, classify, segment patterns in medical images where the advancements in deep learning has been shifted to medical domain also. One of the core abilities of deep learning is that they could learn feature representations automatically from data instead of feeding hand crafted features based on application. In this review, the basics of deep learning is introduced and their success in liver segmentation and lesion detection, classification using CT imaging modality is reviewed and their different network architectures is also discussed. Transfer learning is an interesting approach in deep learning which is also discussed. So, deep learning and CAD system has made a huge impact and has produced enhanced performance in healthcare industry.

Pattern-Recognition: A Comparison of the Performance of Clinicians and Non-Clinicians - with a Note on the Performance of a Computer-Based System

1972 ◽  
Vol 11 (01) ◽  
pp. 32-37 ◽  
Author(s):  
F. T. DE DOMBAL ◽  
J. C. HORROCKS ◽  
J. R. STANILAND ◽  
P. J. GUILLOU
Keyword(s):  
Pattern Recognition ◽  
Computer System ◽  
Computer Aided Diagnosis ◽  
Clinical Interview ◽  
Computer Performance ◽  
Pattern Size ◽  
Computer Aided ◽  
Computer Based ◽  
Aided Diagnosis ◽  
Better Than

This paper describes a series of 10,500 attempts at »pattern-recognition« by two groups of humans and a computer based system. There was little difference between the performances of 11 clinicians and 11 other persons of comparable intellectual capability. Both groups’ performances were related to the pattern-size, the accuracy diminishing rapidly as the patterns grew larger. By contrast the computer system increased its accuracy as the patterns increased in size.It is suggested (a) that clinicians are very little better than others at pattem-recognition, (b) that the clinician is incapable of analysing on a probabilistic basis the data he collects during a traditional clinical interview and examination and (c) that the study emphasises once again a major difference between human and computer performance. The implications as - regards human- and computer-aided diagnosis are discussed.

COMPUTER-AIDED DIAGNOSIS OF GASTRIC LESIONS USING MAGNIFYING NARROW BAND IMAGING ENDOSCOPY

2019 ◽  
Author(s):  
S Kashin ◽  
R Kuvaev ◽  
E Kraynova ◽  
H Edelsbrunner ◽  
O Dunaeva ◽  
...  
Keyword(s):  
Narrow Band ◽  
Narrow Band Imaging ◽  
Computer Aided Diagnosis ◽  
Gastric Lesions ◽  
Computer Aided ◽  
Aided Diagnosis
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