Emphysema discrimination from raw HRCT images by convolutional neural networks

9th International Conference on Electrical and Electronics Engineering, ELECO 2015 --26 November 2015 through 28 November 2015 -- --Emphysema is a chronic lung disease that causes breathlessness. HRCT is the reliable way of visual demonstration of emphysema in patients. The fact that dangerous and widespread nature of the disease require immediate attention of a doctor with a good degree of specialized anatomical knowledge. This necessitates the development of computer-based automatic identification system. This study aims to investigate the deep learning solution for discriminating emphysema subtypes by using raw pixels of input HRCT images of lung. Convolutional Neural Network (CNN) is used as the deep learning method for experiments carried out in the Caffe deep learning framework. As a result, promising percentage of accuracy is obtained besides low processing time. © 2015 Chamber of Electrical Engineers of Turkey

Effective diagnosis of coronary artery disease using the rotation forest ensemble method

PubMedID: 21912972Coronary Artery Disease is a common heart disease related to disorders effecting the heart and blood vessels. Since the disease is one of the leading causes of heart attacks and thus deaths, diagnosis of the disease in its early stages or in cases when patients do not show many of the symptoms yet has considerable importance. In the literature, studies based on computational methods have been proposed to diagnose the disease with readily available and easily collected patient data, and among these studies, the greatest accuracy reached is 89.01%. This paper presents a computational tool based on the Rotation Forest algorithm to effectively diagnose Coronary Artery Disease in order to support clinical decision-making processes. The proposed method utilizes Artificial Neural Networks with the Levenberg-Marquardt back propagation algorithm as base classifiers of the Rotation Forest ensemble method. In this scheme, 91.2% accuracy in diagnosing the disease is accomplished, which is, to the best of our knowledge, the best performance among the computational methods from the literature that use the same data. This paper also presents a comparison of the proposed method with some other classifiers in terms of diagnosis performance of Coronary Artery Disease. © 2011 Springer Science+Business Media, LLC

Discriminative deep belief networks for microarray based cancer classification

Accurate diagnosis of cancer is of great importance due to the global increase in new cancer cases. Cancer researches show that diagnosis by using microarray gene expression data is more effective compared to the traditional methods. This study presents an extensive evaluation of a variant of Deep Belief Networks - Discriminative Deep Belief Networks (DDBN) - in cancer data analysis. This new neural network architecture consists Restricted Boltzman Machines in each layer. The network is trained in two phases; in the first phase the network weights take their initial values by unsupervised greedy layer-wise technique, and in the second phase the values of the network weights are fine-tuned by back propagation algorithm. We included the test results of the model that is conducted over microarray gene expression data of laryngeal, bladder and colorectal cancer. High dimensionality and imbalanced class distribution are two main problems inherent in the gene expression data. To deal with them, two preprocessing steps are applied; Information Gain for selection of predictive genes, and Synthetic Minority Over-Sampling Technique for oversampling the minority class samples. All the results are compared with the corresponding results of Support Vector Machines which has previously been proved to be robust by machine learning studies. In terms of average values DDBN has outperformed SVM in all metrics with accuracy, sensitivity and specificity values of 0.933, 0.950 and 0.905, respectively. © 2017, Scientific Publishers of India. All rights reserved.FDK-2015-4395This study was financially supported by the Cukurova University Research Foundation (Project No: FDK-2015-4395)

Effective Automated Prediction of Vertebral Column Pathologies Based on Logistic Model Tree with SMOTE Preprocessing

PubMedID: 24753003This study develops a logistic model tree based automation system based on for accurate recognition of types of vertebral column pathologies. Six biomechanical measures are used for this purpose: pelvic incidence, pelvic tilt, lumbar lordosis angle, sacral slope, pelvic radius and grade of spondylolisthesis. A two-phase classification model is employed in which the first step is preprocessing the data by use of Synthetic Minority Over-sampling Technique (SMOTE), and the second one is feeding the classifier Logistic Model Tree (LMT) with the preprocessed data. We have achieved an accuracy of 89.73 %, and 0.964 Area Under Curve (AUC) in computer based automatic detection of the pathology. This was validated via a 10-fold-cross-validation experiment conducted on clinical records of 310 patients. The study also presents a comparative analysis of the vertebral column data with the use of several machine learning algorithms. © 2014, Springer Science+Business Media New York

Meta learning on small biomedical datasets

International Conference on Information Science and Applications, ICISA 2016 --15 February 2016 through 18 February 2016 -- --Meta-learning is one of subsections of supervised machine learning that has continuously grown with interests to apply on new data sets in the late years. Meta learning is the process of knowledge that is acquired by the examples. Bagging, dagging, decorate, rotation forest, and filtered classifiers are well known meta-learning algorithms that are performed to compare with these meta-learning algorithms on 8 different biomedical datasets. In these algorithms, the rotation forest had the better results according to F-measurement and ROC Area in most cases. © Springer Science+Business Media Singapore 2016