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Showing papers in "Computer Methods and Programs in Biomedicine in 2017"


Journal ArticleDOI
TL;DR: This work proposes a highly accurate hybrid method for the diagnosis of coronary artery disease that is able to increase the performance of neural network by approximately 10% through enhancing its initial weights using genetic algorithm which suggests better weights for neural network.

343 citations


Journal ArticleDOI
TL;DR: The superiority of the MESAE against the state-of-the-art shallow and deep emotion classifiers has been demonstrated under different sizes of the available physiological instances.

306 citations


Journal ArticleDOI
TL;DR: The two major contributions of the paper constitute a new 3-D approach while applying deep learning technique to extract signature information rooted in both 2D slices and 3D blocks of CT images and an elaborated hand-crated approach of 3D KAZE.

274 citations


Journal ArticleDOI
TL;DR: Developing sleep pattern-related features deem necessary to enhance the performance of this process and implement the state-of-the-art methods indicates that although the accuracy on healthy subjects are remarkable, the results for the main community (patient group) by the quantitative methods are not promising yet.

217 citations


Journal ArticleDOI
TL;DR: The automated sleep scoring scheme propounded herein can eradicate the onus of the clinicians, contribute to the device implementation of a sleep monitoring system, and benefit sleep research.

200 citations


Journal ArticleDOI
TL;DR: Based on the empirical analysis, the proposed ELM-based approach for thyroid cancer detection has promising potential in clinical use, and it can be of assistance as an optional tool for the clinicians.

174 citations


Journal ArticleDOI
TL;DR: Gaussian process (GP)-based classification technique using three kernels namely: linear, polynomial and radial basis kernel is adapted and investigated in comparison to existing techniques such as LDA, QDA and NB.

164 citations


Journal ArticleDOI
TL;DR: A novel WBCs identification system based on deep learning theory is proposed and a high performance W BCsNet can be employed as a pre-trained network.

153 citations


Journal ArticleDOI
TL;DR: Extended experiments reveal that the proposed system can successfully classify Pap smear images performing significantly better when compared with other existing methods, which will be of particular help in early detection of cancer.

135 citations


Journal ArticleDOI
TL;DR: This study has shown that methods which are based on the feature extraction of the biomedical signals are an appropriate approach to predict the health situation of the patients.

133 citations


Journal ArticleDOI
TL;DR: The EM-PCA and the PPCA assisted K-Means algorithm to accomplish the best clustering performance in the majority as well as achieving significant results with both clustering algorithms for all size of T1w MRI images.

Journal ArticleDOI
TL;DR: This review study is conducted to identify different FCM structures used in MDSS designs and determine their contribution to the improvements made in the fields of medical diagnosis and treatment.

Journal ArticleDOI
TL;DR: This is the first study in which the seizure-precursor phenomenon of an epileptic seizure is investigated based on attractor-based analysis of the macroscopic dynamics of the brain.

Journal ArticleDOI
TL;DR: The feasibility of applying a new deep learning based CAD scheme to automatically recognize abdominal section of human body from CT scans and segment SFA and VFA from volumetric CT data with high accuracy or agreement with the manual segmentation results is demonstrated.

Journal ArticleDOI
TL;DR: The proposed decision tree algorithm appears to be an accurate, specific and sensitive model for identifying the presence of CHD, but will require validation in prospective studies.

Journal ArticleDOI
TL;DR: The proposed wavelet transform based representation of spatiotemporal gait variables can efficiently extract relevant features from the different levels of the wavelet towards the classification of Parkinson's and healthy subjects and thus, the present work is a potential candidate for the automatic noninvasive neurodegenerative disease classification.

Journal ArticleDOI
TL;DR: The AVNN metric, using 50 s of window length analysis, showed that it is the most reliable metric to recognize stress level across the four phases of TSST and allows a fine-grained analysis of stress effect as an index of psychological stress and provides an insight into the reaction of the autonomic nervous system to stress.

Journal ArticleDOI
TL;DR: This is the first approach developed to properly consider intra-subject variability for variable selection and classification and it can be applied in other contexts with similar replication-based experimental designs.

Journal ArticleDOI
TL;DR: A pilot study that suggests that the ANNs do not read age-related EEG patterns, but rather invariant features related to the brain's underlying disconnection signature seems to open up new avenues for the development of non-invasive diagnostic testing for the early detection of ASD.

Journal ArticleDOI
TL;DR: Combining texture, shape, and kinetic curve features may be able to differentiate benign from malignant tumors in the liver using the proposed computer-aided diagnosis (CAD) system.

Journal ArticleDOI
TL;DR: The evaluation showed that the game could be used as a useful tool to motivate the patients during rehabilitation sessions, and next step is to evaluate its effectiveness for stroke patients, in order to verify if the interface and game exercises contribute into the motor rehabilitation treatment progress.

Journal ArticleDOI
TL;DR: Optally tuned PID controllers provide a fast induction time with an acceptable overshoot and a satisfactory disturbance rejection performance during maintenance, which makes them a very good tool for comparison when other control algorithms are developed.

Journal ArticleDOI
TL;DR: This improved supervised artery and vein classification method in retinal image serves as a potentially important tool for the early diagnosis of various diseases, including diabetic retinopathy and cardiovascular diseases.

Journal ArticleDOI
TL;DR: A novel scheme for blood vessel segmentation based on discriminative dictionary learning (DDL) and sparse representation has been proposed, which can achieve comparable results to existing methods and decrease false positive vessels in abnormal retinal images with pathological regions.

Journal ArticleDOI
TL;DR: A decision tree model to identify the risk factors associated with hypertension that maybe used to develop programs for hypertension management is developed.

Journal ArticleDOI
TL;DR: This application provides an efficient workflow for model creation and helps standardize the process, and it is hoped this would help promote personalized applications in musculoskeletal biomechanics, including larger sample size studies, and might also represent a basis for future developments for specific applications.

Journal ArticleDOI
TL;DR: A computer-aided diagnosis (CAD) system based on quantitative magnetic resonance imaging (MRI) features was developed to evaluate the malignancy of diffuse gliomas, which are central nervous system tumors.

Journal ArticleDOI
TL;DR: A robust automated end-to-end classifier for biomedical images based on a domain transferred deep convolutional neural network model that shows a highly reliable and accurate performance which has been confirmed on several public biomedical image datasets.

Journal ArticleDOI
TL;DR: The empirical results indicate that the proposed lightweight real-time sliding window-based Max-Min Difference algorithm for QRS detection from Lead II ECG signals yields a high accuracy rate and exhibits efficient computational complexity at the order of O(n), where n is the length of an ECG signal.

Journal ArticleDOI
TL;DR: A comparative analysis of various nature inspired algorithms to select optimal features/variables required for aiding in the classification of affected patients from the rest shows Binary Bat Algorithm outperformed traditional techniques like Particle Swarm Optimization (PSO), Genetic Algorithm and Modified Cuckoo Search Algorithm with a competitive recognition rate on the dataset of selected features.