scholarly journals A Computer-Aided Diagnosis System for Lung Cancer Detection with Automatic Region Growing, Multistage Feature Selection and Neural Network Classifier

2019 ◽  
Vol 9 (1S) ◽  
pp. 409-413
Keyword(s):  
Neural Network ◽  
Cancer Detection ◽  
Region Growing ◽  
Calcium Deposition ◽  
Lung Nodules ◽  
Neural Network Classifier ◽  
Artificial Neural Network Ann ◽  
Sensitivity Specificity ◽  
Lung Cancer Detection ◽  
Aided Diagnosis

An effective automatic region growing was developed in this work for the segmentation of suspected lung nodules from the Computed Tomography (CT) lung images. After the segmentation of the suspected lung nodules the eccentricity and area features were calculated to eliminate line like structures and tiny clusters below 3mm. The centroid analysis, contrast, autocorrelation and homogeneity features were extracted for the suspected lung nodules. The extracted features were trained and tested with Artificial Neural Network (ANN) to remove the blood vessels and calcifications (calcium deposition in the lungs). This work was carried out on 106 patients images retrospectively collected from Bharat Scans, Chennai, which had 56 cancerous nodules and 745 non-cancerous nodules (size greater than 3 mm). The proposed work yielded sensitivity, specificity and accuracy of 100%, 93% and 94%, respectively.

scholarly journals Centered Kernel Alignment Enhancing Neural Network Pretraining for MRI-Based Dementia Diagnosis

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
David Cárdenas-Peña ◽  
Diego Collazos-Huertas ◽  
German Castellanos-Dominguez
Keyword(s):  
Neural Network ◽  
Characteristic Curve ◽  
Principal Component ◽  
Imaging Data ◽  
Resonance Imaging ◽  
Dementia Diagnosis ◽  
Target Matrix ◽  
Computer Aided ◽  
Artificial Neural Network Ann ◽  
Aided Diagnosis

Dementia is a growing problem that affects elderly people worldwide. More accurate evaluation of dementia diagnosis can help during the medical examination. Several methods for computer-aided dementia diagnosis have been proposed using resonance imaging scans to discriminate between patients with Alzheimer’s disease (AD) or mild cognitive impairment (MCI) and healthy controls (NC). Nonetheless, the computer-aided diagnosis is especially challenging because of the heterogeneous and intermediate nature of MCI. We address the automated dementia diagnosis by introducing a novel supervised pretraining approach that takes advantage of the artificial neural network (ANN) for complex classification tasks. The proposal initializes an ANN based on linear projections to achieve more discriminating spaces. Such projections are estimated by maximizing the centered kernel alignment criterion that assesses the affinity between the resonance imaging data kernel matrix and the label target matrix. As a result, the performed linear embedding allows accounting for features that contribute the most to the MCI class discrimination. We compare the supervised pretraining approach to two unsupervised initialization methods (autoencoders and Principal Component Analysis) and against the best four performing classification methods of the 2014CADDementiachallenge. As a result, our proposal outperforms all the baselines (7% of classification accuracy and area under the receiver-operating-characteristic curve) at the time it reduces the class biasing.

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