A Binary Classification of Cardiovascular Abnormality Using Time-Frequency Features of Cardio-mechanical Signals

The importance of automated classification of histopathological images has been increasingly recognized for effective processing of large volumes of data in the era of digital pathology for new discovery of disease mechanism. This paper presents a deep-learning approach that extracts time-frequency features of H&E stained tissue images for classification by long short-term memory networks. Using two large public databases of colorectal-cancer and heart-failure H&E stained tissue images, the proposed approach outperforms several state-of-the-art benchmark classification methods, including support vector machines and convolutional neural networks in terms of several statistical measures.

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Classification of spot-welded joint strength using ultrasonic signal time-frequency features and PSO-SVM method

Ultrasonics ◽

10.1016/j.ultras.2018.08.014 ◽

2019 ◽

Vol 91 ◽

pp. 161-169 ◽

Cited By ~ 18

Author(s):

Xiaokai Wang ◽

Shanyue Guan ◽

Lin Hua ◽

Bin Wang ◽

Ximing He

Keyword(s):

Joint Strength ◽

Welded Joint ◽

Ultrasonic Signal ◽

Time Frequency ◽

Frequency Features ◽

Signal Time ◽

Spot Welded

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Extracting Time-Frequency Features of Images for Robust LSTM-based Classification of H&E Stained Tissue

10.36227/techrxiv.13325162.v1 ◽

2020 ◽

Author(s):

Tuan Pham

Keyword(s):

Short Term Memory ◽

Digital Pathology ◽

Support Vector ◽

Automated Classification ◽

Time Frequency ◽

Statistical Measures ◽

Vector Machines ◽

Histopathological Images ◽

Frequency Features

The importance of automated classification of histopathological images has been increasingly recognized for effective processing of large volumes of data in the era of digital pathology for new discovery of disease mechanism. This paper presents a deep-learning approach that extracts time-frequency features of H&E stained tissue images for classification by long short-term memory networks. Using two large public databases of colorectal-cancer and heart-failure H&E stained tissue images, the proposed approach outperforms several state-of-the-art benchmark classification methods, including support vector machines and convolutional neural networks in terms of several statistical measures.

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Can a Smartphone Diagnose Parkinson Disease? A Deep Neural Network Method and Telediagnosis System Implementation

Parkinson s Disease ◽

10.1155/2017/6209703 ◽

2017 ◽

Vol 2017 ◽

pp. 1-11 ◽

Cited By ~ 11

Author(s):

Y. N. Zhang

Keyword(s):

Machine Learning ◽

Nearest Neighbor ◽

Low Cost ◽

Walking Test ◽

Time Frequency ◽

Advantages And Disadvantages ◽

Stacked Autoencoders ◽

Knn Classifier ◽

Frequency Features

Parkinson’s disease (PD) is primarily diagnosed by clinical examinations, such as walking test, handwriting test, and MRI diagnostic. In this paper, we propose a machine learning based PD telediagnosis method for smartphone. Classification of PD using speech records is a challenging task owing to the fact that the classification accuracy is still lower than doctor-level. Here we demonstrate automatic classification of PD using time frequency features, stacked autoencoders (SAE), and K nearest neighbor (KNN) classifier. KNN classifier can produce promising classification results from useful representations which were learned by SAE. Empirical results show that the proposed method achieves better performance with all tested cases across classification tasks, demonstrating machine learning capable of classifying PD with a level of competence comparable to doctor. It concludes that a smartphone can therefore potentially provide low-cost PD diagnostic care. This paper also gives an implementation on browser/server system and reports the running time cost. Both advantages and disadvantages of the proposed telediagnosis system are discussed.

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