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
Seyedeh Somayeh Naghibi; Ali Fallah; Ali Maleki; Farnaz Ghassemi
Volume 13, Issue 3 , October 2019, , Pages 247-257
Abstract
The correct prediction of the optimal motor trajectory is necessary for movement rehabilitation and control systems such as functional electrical stimulation and robotic therapy. It seems that human reaching movements are composed of a set of submovements, each of which is a correction of the overall ...
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The correct prediction of the optimal motor trajectory is necessary for movement rehabilitation and control systems such as functional electrical stimulation and robotic therapy. It seems that human reaching movements are composed of a set of submovements, each of which is a correction of the overall movement trajectory. Therefore, it is possible to interpret complex movements, learning, adaptability and other features of the motion control system using submovements. The purpose of this study is predicting and generating planar reaching movements using a realistic model similar to the actual mechanism of human movement and based on the submovement. The data used consists of different replications of four types of planar movement Performed by three healthy subjects. After the preprocessing and phasing, the movements decomposed to minimum-jerk submovement. In the next step, the training of three distinct neural networks was carried out to learn the submovement parameters including the amplitude, duration, and initiation time. Finally, the ANNs were combined to form a closed-loop model that generated accurate reaching movements based on the error correction. The target access rate for all predicted movements by the closed loop model was 100%. Also, the mean distance to the target, the VAF, and the mean MSE error between the predicted and main movement trajectory showed that the predicted movements are a good approximation of the main movements. The results showed that when trained neural networks with submovements, were placed in a closed loop model, they were able to predict proper submovements for complete access to targets due to the compensation of propagated errors from the previous steps. The results of this study can be used to improve motor rehabilitation methods.
