Medical Ultrasound / Diagnostic Sonography / Ultrasonography
Mahsa Arab; Ali Fallah; Saeid Rashidi; Maryam Mehdizadeh Dastjerdi; Nasrin Ahmadinejad
Volume 17, Issue 2 , September 2023, , Pages 140-150
Abstract
Breast cancer stands as the most prevalent form of cancer among women, with over 80% of early-stage breast abnormalities being benign. Timely detection is therefore crucial for prompt intervention. Ultrasound Radio Frequency (US RF) signals represent a non-invasive, and real-time screening method for ...
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Breast cancer stands as the most prevalent form of cancer among women, with over 80% of early-stage breast abnormalities being benign. Timely detection is therefore crucial for prompt intervention. Ultrasound Radio Frequency (US RF) signals represent a non-invasive, and real-time screening method for breast cancer, offering advantages in tissue differentiation and cost-effectiveness without requiring additional equipment. This research aims to present an intelligent approach for the classification of benign, suspicious, and malignant breast lesions based on effective features extracted from the time series. The dataset, registered as USRFTS, comprises 170 instances recorded from 88 patients. The proposed methodology encompasses four key phases: pre-processing, feature extraction, feature selection, and classification. In the pre-processing phase, B-mode images are reconstructed from US RF time series, and a radiologist manually selects the Region of Interest (ROI) in each image. Subsequently, diverse features in the time and frequency domains are extracted from each ROI during the feature extraction stage. The ant colony method is employed for the selection of impactful features. The dataset is then subjected to evaluation using classifiers such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Decision Tree (DT), Linear Discriminant Analysis (LDA), and a reference classification method (RCM). The results demonstrate a maximum classification accuracy of 94.95% for two classes and 93.33% for three classes
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
davoud saadati; Sattar Mirzakuchaki
Volume 16, Issue 4 , March 2023, , Pages 61-70
Abstract
Analysis and examination of sound of organs can be utilized in order to diagnose various diseases and abnormal conditions. Diagnostic methods based on audio signal processing are non-invasive and inexpensive and can be especially useful in under-developed countries, where inadequate medical specialists ...
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Analysis and examination of sound of organs can be utilized in order to diagnose various diseases and abnormal conditions. Diagnostic methods based on audio signal processing are non-invasive and inexpensive and can be especially useful in under-developed countries, where inadequate medical specialists and equipment has led to high fatality rates. Development of accessible methods based on machine learning can aid with early diagnosis. we used a convolutional network to attain the advantages of transfer learning. In previous studies, models have been proposed that feed spectrograms with frequency characteristics as inputs to the convolutional network. In this article, we propose a model which additionally employs a recurrent representation (Recurrence plot) that reflects the temporal characteristics of the sound. The audio data sequence is investigated by adding the temporal attention mechanism and the bi-directional recurrent gates for weighting data according to its informational value. Data used in this article is from the ICBHI lung sound database. The presented model was able to classify lung sounds into three categories: healthy, chronic obstructive pulmonary disease (COPD), and other diseases with an accuracy of 97%, which shows the superiority of the proposed method compared to results obtained from previous methods on the same database.
Abolfazl Tabatabaei; Vali Derhami; Razieh Sheikhpour; Mohammad-Reza Pajoohan
Volume 13, Issue 4 , December 2019, , Pages 337-348
Abstract
Feature selection is a well-known preprocessing technique in machine learning, data mining and especially bioinformatics microarray analysis with a high-dimension, low-sample-size (HDLSS) data. The diagnosis of genes responsible for disease using microarray data is an important issue to promoting knowledge ...
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Feature selection is a well-known preprocessing technique in machine learning, data mining and especially bioinformatics microarray analysis with a high-dimension, low-sample-size (HDLSS) data. The diagnosis of genes responsible for disease using microarray data is an important issue to promoting knowledge about the mechanism of disease and improves the way of dealing with the disease. In feature selection methods based on information theory, which cover a wide range of feature selection methods, the concept of entropy is used to define criteria for relevance, redundancy and complementarity. In this paper, we propose a new relevancy criterion based on the concept of pure continuity rather than the concept of entropy. In the proposed method, to control and reduce redundancy, the relevancy between a feature and each class is separately examined, while in most of the filter methods the value of a feature is measured based on its relation to the entire class. This solution allows us to identify the most efficient features (genes) of each class separately, while identifying common features (genes) is also possible. Discretization is another challenge in some available techniques. Using a homomorphism transformation in proposed method avoids engaging with discretization complexities, while taking advantages of it. Seven types of cancer microarrays with three types of classification models (e.g. NB, KNN and SVM) are used to establish a comparison between the proposed method and other relevant methods. The results confirm the efficiency of the proposed method in the term of accuracy and number of selected genes as two parameters of classification.
Saeed Ghodsi; Hoda Mohammadzade; Hamid Aghajan
Volume 13, Issue 3 , October 2019, , Pages 189-207
Abstract
Different perceptual, cognitive and emotional situations results in a kind of information flow in the brain by means of coordinated neuronal oscillations. Analysing these oscillations, especially synchronizations of different brain regions, can illustrate the brains response in the aforementioned situations. ...
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Different perceptual, cognitive and emotional situations results in a kind of information flow in the brain by means of coordinated neuronal oscillations. Analysing these oscillations, especially synchronizations of different brain regions, can illustrate the brains response in the aforementioned situations. In the literature, connectivity between brain regions is divided into the three groups of structural, effective and functional, s.t. the first one refers to the connectivity between nearby regions, while the second and third ones focus on the synchronization of oscillations of arbitrary located regions. Although EEG is not the best choice for analyzing functional and effective connectivity between brain regions due to its relatively poor spatial resolution, extracting its statistical features may be helpful in the analysis of synchronization of brain oscillations. In this paper, a novel framework for the prediction of seizure occurrence using EEG signals is proposed which utilizes the Granger causality approach in frequency domain to measure synchronization of EEG signals in the Inter-ictal and Pre-ictal time periods. Afterwards, a Logistic Regression classifier with Lasso regularization is used to discriminate the samples extracted from these two periods. At last, if a predefined number of consecutive samples are labled as Pre-ictals, a seizure occurrence alarm is issued. Experimental simulations on the CHB-MIT dataset resulted in 95.03% sensitivity and 0.14/hour false prediction rate, for 10min prediction horizon, which demonstrates effectiveness of our proposed method compared to the state-of-the-arts.
