The exploration machine: a novel method for analyzing high-dimensional data in computer-aided diagnosis

2009 ◽  
Author(s):  
Axel Wismüller
Keyword(s):  
High Dimensional Data ◽  
Computer Aided Diagnosis ◽  
High Dimensional ◽  
Computer Aided ◽  
Novel Method ◽  
Aided Diagnosis

scholarly journals Mammographic CAD: Correlation of regions in ipsilateral views - a pilot study

2009 ◽  
Vol 13 (3) ◽  
pp. 48 ◽  
Author(s):  
J Padayachee ◽  
M J Alport ◽  
W ID Rae
Keyword(s):  
Pilot Study ◽  
Characteristic Curve ◽  
Computer Aided Diagnosis ◽  
Similarity Metric ◽  
Computer Aided ◽  
Similarity Maps ◽  
Novel Method ◽  
Occurrence Matrix ◽  
Aided Diagnosis ◽  
Suspicious Feature

Background: Radiologists analyse both standard mammographic views of a breast to confirm the presence of abnormalities and reduce false-positives. However, at present no computer-aided diagnosis system uses ipsilateral mammograms to confirm the presence of suspicious features. Aim: The aim of this study was to develop image-processing algorithms that can be used to match a suspicious feature from one mammographic view to the same feature in another mammographic view of the same breast. This algorithm can be incorporated into a computer-aided diagnosis package to confirm the presence of suspicious features. Method: The algorithms were applied to 68 matched pairs of cranio-caudal and mediolateral-oblique mammograms. The results of this pilot study take the form of maps of similarity. A novel method of evaluating the similarity maps is presented, using the area under the receiver operating characteristic curve (AUC) and the contrast (C) between the area of the matched region and the background of the similarity map. Results and Conclusions: The first matching algorithm (using texture measures extracted from a grey-level co-occurrence matrix (GLCM) and a Euclidean distance similarity metric) achieved an average AUC=0.80±0.17 with an average C=0.46±0.26. The second algorithm (using GLCMs and a mutual information similarity metric) achieved an average AUC=0.77±0.25 with an average C=0.50±0.42. The latter algorithm also performed remarkably well with the matching of malignant masses and achieved an average AUC=0.96±0.05 with an average C=0.90±0.21.

scholarly journals Hybrid CNN assisted Computer Aided Diagnosis System for Glaucoma Detection and Classification: GlaucoNet+

2019 ◽  
Vol 9 (1) ◽  
pp. 3060-3072
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Computer Aided Diagnosis ◽  
High Dimensional ◽  
Support Vector ◽  
Linear Discriminant ◽  
Glaucoma Detection ◽  
Computer Aided ◽  
Deep Learning Model ◽  
Aided Diagnosis

The exponential rise in technologies has revitalized academia-industries to achieve more efficient computer aided diagnosis systems. It becomes inevitable especially for Glaucoma detection which has been increasing with vast pace globally. Most of the existing approaches employs morphological features like optical disk and optical cup information, optical cup to disk ratio etc; however enabling optimal detection of such traits has always been challenge for researchers. On the other hand, in the last few years deep learning methods have gained widespread attention due to its ability to exploit fine grained features of images to make optimal classification decision. However, reliance of such methods predominantly depends on the presence of deep features demanding suitable feature extraction method. To achieve it major existing approaches extracts full-image features that with high dimensional kernel generates gigantically huge features, making classification computationally overburdened. Therefore, retaining optimal balance between deep features and computational overhead is of utmost significance for glaucoma detection and classification. With this motive, in this paper a novel hybrid deep learning model has been developed for Glaucoma detection and classification. The proposed Hybrid CNN model embodies Stacked Auto-Encoder (SAE) with transferable learning model AlexNet that extracts high dimensional features to make further two-class classification. To achieve computational efficiency, In addition to the classical ReLu and dropout (50%), we used Principle Component Analysis (PCA) and Linear Discriminant Analysis (LDA) algorithms. We applied 10-fold cross validation assisted Support Vector Machine classifier to perform two-class classification; Glaucomatous and Normal fundus images. Simulation results affirmed that the proposed Hybrid deep learning model with LDA feature selection and SVM-Poly classification achieves the maximum accuracy of 98.8%, precision 97.5%, recall 97.5% and F-Measure of 97.8%.

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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