Automatic Muscle Artifacts Identification and Removal from Single-Channel EEG Using Wavelet Transform with Meta-Heuristically Optimized Non-Local Means Filter

Phadikar, Souvik; Sinha, Nidul; Ghosh, Rajdeep; Ghaderpour, Ebrahim

doi:10.3390/s22082948

Cited by 29 publications

(24 citation statements)

References 52 publications

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“…SVM has recently gained much popularity, being widely used in large margin classification problems, including medical diagnosis areas [47,48], machine learning [49], and pattern recognition [50,51]. SVM has also been successfully used in many other applications, such as signature and text recognition, face expression recognition, speech recognition, biometrics, emotion recognition, and several content-based applications, as detailed in [52][53][54]. Naïve Bayes belong to the family of probabilistic networks based on Bayes' theorem.…”

Section: Classificationmentioning

confidence: 99%

Lung Cancer Prediction Using Robust Machine Learning and Image Enhancement Methods on Extracted Gray-Level Co-Occurrence Matrix Features

et al. 2022

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In the present era, cancer is the leading cause of demise in both men and women worldwide, with low survival rates due to inefficient diagnostic techniques. Recently, researchers have been devising methods to improve prediction performance. In medical image processing, image enhancement can further improve prediction performance. This study aimed to improve lung cancer image quality by utilizing and employing various image enhancement methods, such as image adjustment, gamma correction, contrast stretching, thresholding, and histogram equalization methods. We extracted the gray-level co-occurrence matrix (GLCM) features on enhancement images, and applied and optimized vigorous machine learning classification algorithms, such as the decision tree (DT), naïve Bayes, support vector machine (SVM) with Gaussian, radial base function (RBF), and polynomial. Without the image enhancement method, the highest performance was obtained using SVM, polynomial, and RBF, with accuracy of (99.89%). The image enhancement methods, such as image adjustment, contrast stretching at threshold (0.02, 0.98), and gamma correction at gamma value of 0.9, improved the prediction performance of our analysis on 945 images provided by the Lung Cancer Alliance MRI dataset, which yielded 100% accuracy and 1.00 of AUC using SVM, RBF, and polynomial kernels. The results revealed that the proposed methodology can be very helpful to improve the lung cancer prediction for further diagnosis and prognosis by expert radiologists to decrease the mortality rate.

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Section: Classificationmentioning

confidence: 99%

Lung Cancer Prediction Using Robust Machine Learning and Image Enhancement Methods on Extracted Gray-Level Co-Occurrence Matrix Features

et al. 2022

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“…Sánchez et al [8] deal with motion artifacts in OCT imaging. Phadikar et al [9] proposed a solution for muscle artifacts removal. Interesting solutions for segmentation were proposed by Ahmad et al [10] and Qadri et al [11].…”

Section: Related Workmentioning

confidence: 99%

“…Inspired by MiniVGGNet, presented in [31] which uses the main architecture of the VGG network [32], we created CNN models with different convolution layers (3,5,7,9,11) (as shown in Table 2). Afterward, the proposed lightweight model, with only five convolution layers, was modified to apply to our new augmented dataset.The 5-layer (v1) and 5-layer (v2) network architecture diagram is shown in Figure 3 and Table 3 respectively.…”

Section: Proposed Modelsmentioning

confidence: 99%

Fast and Efficient Method for Optical Coherence Tomography Images Classification Using Deep Learning Approach

Ara

Matiolański

Dziech

et al. 2022

Sensors

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The use of optical coherence tomography (OCT) in medical diagnostics is now common. The growing amount of data leads us to propose an automated support system for medical staff. The key part of the system is a classification algorithm developed with modern machine learning techniques. The main contribution is to present a new approach for the classification of eye diseases using the convolutional neural network model. The research concerns the classification of patients on the basis of OCT B-scans into one of four categories: Diabetic Macular Edema (DME), Choroidal Neovascularization (CNV), Drusen, and Normal. Those categories are available in a publicly available dataset of above 84,000 images utilized for the research. After several tested architectures, our 5-layer neural network gives us a promising result. We compared them to the other available solutions which proves the high quality of our algorithm. Equally important for the application of the algorithm is the computational time, which is reduced by the limited size of the model. In addition, the article presents a detailed method of image data augmentation and its impact on the classification results. The results of the experiments were also presented for several derived models of convolutional network architectures that were tested during the research. Improving processes in medical treatment is important. The algorithm cannot replace a doctor but, for example, can be a valuable tool for speeding up the process of diagnosis during screening tests.

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“…Background. Brain-computer interface is an important issue in biosignal and it enables users to communicate directly with computers using brain signals [ 97 , 98 , 148 , 149 , 150 ]. There are three general types of BCI, non-invasive BCI, invasive BCI, and partially invasive BCI.…”

Section: Applicationsmentioning

confidence: 99%

Deep Unsupervised Domain Adaptation with Time Series Sensor Data: A Survey

Shi

Ying

Yang

2022

Sensors

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Sensors are devices that output signals for sensing physical phenomena and are widely used in all aspects of our social production activities. The continuous recording of physical parameters allows effective analysis of the operational status of the monitored system and prediction of unknown risks. Thanks to the development of deep learning, the ability to analyze temporal signals collected by sensors has been greatly improved. However, models trained in the source domain do not perform well in the target domain due to the presence of domain gaps. In recent years, many researchers have used deep unsupervised domain adaptation techniques to address the domain gap between signals collected by sensors in different scenarios, i.e., using labeled data in the source domain and unlabeled data in the target domain to improve the performance of models in the target domain. This survey first summarizes the background of recent research on unsupervised domain adaptation with time series sensor data, the types of sensors used, the domain gap between the source and target domains, and commonly used datasets. Then, the paper classifies and compares different unsupervised domain adaptation methods according to the way of adaptation and summarizes different adaptation settings based on the number of source and target domains. Finally, this survey discusses the challenges of the current research and provides an outlook on future work. This survey systematically reviews and summarizes recent research on unsupervised domain adaptation for time series sensor data to provide the reader with a systematic understanding of the field.

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Automatic Muscle Artifacts Identification and Removal from Single-Channel EEG Using Wavelet Transform with Meta-Heuristically Optimized Non-Local Means Filter

Cited by 29 publications

References 52 publications

Lung Cancer Prediction Using Robust Machine Learning and Image Enhancement Methods on Extracted Gray-Level Co-Occurrence Matrix Features

Lung Cancer Prediction Using Robust Machine Learning and Image Enhancement Methods on Extracted Gray-Level Co-Occurrence Matrix Features

Fast and Efficient Method for Optical Coherence Tomography Images Classification Using Deep Learning Approach

Deep Unsupervised Domain Adaptation with Time Series Sensor Data: A Survey

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