Fuzzy Recurrence Plots

2021

Sci Rep

Self Cite

Automated analysis of physiological time series is utilized for many clinical applications in medicine and life sciences. Long short-term memory (LSTM) is a deep recurrent neural network architecture used for classification of time-series data. Here time–frequency and time–space properties of time series are introduced as a robust tool for LSTM processing of long sequential data in physiology. Based on classification results obtained from two databases of sensor-induced physiological signals, the proposed approach has the potential for (1) achieving very high classification accuracy, (2) saving tremendous time for data learning, and (3) being cost-effective and user-comfortable for clinical trials by reducing multiple wearable sensors for data recording.

Section: Resultsmentioning

confidence: 99%

“…These fuzzy membership grades can be determined using the fuzzy c -means algorithm 40 . An FRP, denoted by , is defined as 25 where is the fuzzy membership of similarity between and .…”

Section: Time–frequency and Time–space Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Time–frequency time–space LSTM for robust classification of physiological signals

2021

Sci Rep

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“…Next, given a time series, an embedding dimension, and a time delay, the phase space of the corresponding dynamical system can be constructed and represented with a collection of vectors . Elements of an FRP are defined as 24 where is the fuzzy membership of similarity between and .…”

Section: Methodsmentioning

confidence: 99%

Time-frequency time-space long short-term memory networks for image classification of histopathological tissue

2021

Sci Rep

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Image analysis in histopathology provides insights into the microscopic examination of tissue for disease diagnosis, prognosis, and biomarker discovery. Particularly for cancer research, precise classification of histopathological images is the ultimate objective of the image analysis. Here, the time-frequency time-space long short-term memory network (TF-TS LSTM) developed for classification of time series is applied for classifying histopathological images. The deep learning is empowered by the use of sequential time-frequency and time-space features extracted from the images. Furthermore, unlike conventional classification practice, a strategy for class modeling is designed to leverage the learning power of the TF-TS LSTM. Tests on several datasets of histopathological images of haematoxylin-and-eosin and immunohistochemistry stains demonstrate the strong capability of the artificial intelligence (AI)-based approach for producing very accurate classification results. The proposed approach has the potential to be an AI tool for robust classification of histopathological images.

“…A concern with selected values for the threshold is that these values are found very sensitive in many applications [26]. To alleviate the difficulty in using the similarity threshold for constructing an RP, the concept of a fuzzy recurrence plot was introduced [27]. The construction of an FRP is based on the principles of fuzzy similarity inference, it is therefore free from the use of the similarity threshold.…”

Section: B Generation Of Recurrence Images As Pseudo-labeled Data From Original Ctmentioning

confidence: 99%

From Raw Pixels to Recurrence Image for Deep Learning of Benign and Malignant Mediastinal Lymph Nodes on Computed Tomography

2021

IEEE Access

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Lung cancer causes the most cancer deaths worldwide and has one of the lowest five-year survival rates of all cancer types. It is reported that more than half of patients with lung cancer die within one year of being diagnosed. Because mediastinal lymph node status is the most important factor for the treatment and prognosis of lung cancer, the aim of this study is to improve the predictive value in assessing the computed tomography (CT) of mediastinal lymph-node malignancy in patients with primary lung cancer. This paper introduces a new method for creating pseudo-labeled images of CT regions of mediastinal lymph nodes by using the concept of recurrence analysis in nonlinear dynamics for the transfer learning. Pseudolabeled images of original CT images are used as input into deep-learning models. Three popular pretrained convolutional neural networks (AlexNet, SqueezeNet, and DenseNet-201) were used for the implementation of the proposed concept for the classification of benign and malignant mediastinal lymph nodes using a public CT database. In comparison with the use of the original CT data, the results show the high performance of the transformed images for the task of classification. Three pretrained convolutional neural networks that are AlexNet, SqueezeNet, and DenseNet201 were trained and tested with the transformed images. Classification accuracies and areas under the receiver operating characteristic curve obtained from the ten-fold cross-validation are 93% and 0.97, 96% and 0.99, and 100% and 1 for the SqueezeNet, AlexNet, and DenseNet201, respectively. The proposed method has the potential for differentiating benign from malignant mediastinal lymph nodes on CT, and may provide a new way for studying lung cancer using radiology imaging.INDEX TERMS Lung cancer, lymph nodes, computed tomography, recurrence analysis, convolutional neural networks, transfer learning.