scholarly journals From raw ion mobility measurements to disease classification: a comparison of analysis processes

2015 ◽  
Author(s):  
Salome Horsch ◽  
Dominik Kopczynski ◽  
Jörg Ingo Baumbach ◽  
Jörg Rahnenführer ◽  
Sven Rahmann
Keyword(s):  
Ion Mobility ◽  
Peak Detection ◽  
Disease Classification ◽  
Classification Algorithms ◽  
Exhaled Breath ◽  
Major Goal ◽  
Analysis Process ◽  
Automated Algorithms ◽  
Better Than

Ion mobility spectrometry (IMS) is a technology for the detection of volatile compounds in the air of exhaled breath that is increasingly used in medical applications. One major goal is to classify patients into disease groups, for example diseased versus healthy, from simple breath samples. Raw IMS measurements are data matrices in which peak regions representing the compounds have to be identified and quantified. A typical analysis process consists of pre-processing and peak detection in single experiments, peak clustering to obtain consensus peaks across several experiments, and classification of samples based on the resulting multivariate peak intensities. Recently several automated algorithms for peak detection and peak clustering have been introduced, in order to overcome the current need for human-based analysis that is slow, subjective and sometimes not reproducible. We present an unbiased comparison of a multitude of combinations of peak processing and multivariate classification algorithms on a disease dataset. The specific combination of the algorithms for the different analysis steps determines the classification accuracy, with the encouraging result that certain fully-automated combinations perform even better than current manual approaches.

scholarly journals From raw ion mobility measurements to disease classification: a comparison of analysis processes

2015 ◽  
Author(s):  
Salome Horsch ◽  
Dominik Kopczynski ◽  
Jörg Ingo Baumbach ◽  
Jörg Rahnenführer ◽  
Sven Rahmann
Keyword(s):  
Ion Mobility ◽  
Peak Detection ◽  
Disease Classification ◽  
Classification Algorithms ◽  
Exhaled Breath ◽  
Major Goal ◽  
Analysis Process ◽  
Automated Algorithms ◽  
Better Than

Ion mobility spectrometry (IMS) is a technology for the detection of volatile compounds in the air of exhaled breath that is increasingly used in medical applications. One major goal is to classify patients into disease groups, for example diseased versus healthy, from simple breath samples. Raw IMS measurements are data matrices in which peak regions representing the compounds have to be identified and quantified. A typical analysis process consists of pre-processing and peak detection in single experiments, peak clustering to obtain consensus peaks across several experiments, and classification of samples based on the resulting multivariate peak intensities. Recently several automated algorithms for peak detection and peak clustering have been introduced, in order to overcome the current need for human-based analysis that is slow, subjective and sometimes not reproducible. We present an unbiased comparison of a multitude of combinations of peak processing and multivariate classification algorithms on a disease dataset. The specific combination of the algorithms for the different analysis steps determines the classification accuracy, with the encouraging result that certain fully-automated combinations perform even better than current manual approaches.

scholarly journals Gravitation Theory Based Model for Multi-Label Classification

2017 ◽  
Vol 12 (5) ◽  
pp. 689
Author(s):  
Liwen Peng ◽  
Yongguo Liu
Keyword(s):  
High Performance ◽  
Text Categorization ◽  
Classification Algorithms ◽  
Universal Gravitation ◽  
The Past ◽  
Proposed Model ◽  
Series Of Experiments ◽  
Music Information ◽  
Better Than

The past decade has witnessed the growing popularity in multi-label classification algorithms in the fields like text categorization, music information retrieval, and the classification of videos and medical proteins. In the meantime, the methods based on the principle of universal gravitation have been extensively used in the classification of machine learning owing to simplicity and high performance. In light of the above, this paper proposes a novel multi-label classification algorithm called the interaction and data gravitation-based model for multi-label classification (ITDGM). The algorithm replaces the interaction between two objects with the attraction between two particles. The author carries out a series of experiments on five multi-label datasets. The experimental results show that the ITDGM performs better than some well-known multi-label classification algorithms. The effect of the proposed model is assessed by the example-based F1-Measure and Label-based micro F1-measure.

scholarly journals Plant Disease Classification: A Comparative Evaluation of Convolutional Neural Networks and Deep Learning Optimizers

Plants ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1319
Author(s):  
Muhammad Hammad Saleem ◽  
Johan Potgieter ◽  
Khalid Mahmood Arif
Keyword(s):  
Deep Learning ◽  
Comparative Evaluation ◽  
Plant Disease ◽  
Performance Metrics ◽  
State Of The Art ◽  
Disease Classification ◽  
Agricultural Applications ◽  
Learning Architectures ◽  
Better Than

Recently, plant disease classification has been done by various state-of-the-art deep learning (DL) architectures on the publicly available/author generated datasets. This research proposed the deep learning-based comparative evaluation for the classification of plant disease in two steps. Firstly, the best convolutional neural network (CNN) was obtained by conducting a comparative analysis among well-known CNN architectures along with modified and cascaded/hybrid versions of some of the DL models proposed in the recent researches. Secondly, the performance of the best-obtained model was attempted to improve by training through various deep learning optimizers. The comparison between various CNNs was based on performance metrics such as validation accuracy/loss, F1-score, and the required number of epochs. All the selected DL architectures were trained in the PlantVillage dataset which contains 26 different diseases belonging to 14 respective plant species. Keras with TensorFlow backend was used to train deep learning architectures. It is concluded that the Xception architecture trained with the Adam optimizer attained the highest validation accuracy and F1-score of 99.81% and 0.9978 respectively which is comparatively better than the previous approaches and it proves the novelty of the work. Therefore, the method proposed in this research can be applied to other agricultural applications for transparent detection and classification purposes.

scholarly journals BALSAM—An Interactive Online Platform for Breath Analysis, Visualization and Classification

Metabolites ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. 393
Author(s):  
Philipp Weber ◽  
Josch Konstantin Pauling ◽  
Markus List ◽  
Jan Baumbach
Keyword(s):  
Ion Mobility ◽  
Capillary Column ◽  
Low Cost ◽  
Good Practice ◽  
Automated Analysis ◽  
Breath Analysis ◽  
Peak Detection ◽  
Analysis Process ◽  
Analysis Platform ◽  
Web Platform

The field of breath analysis lacks a fully automated analysis platform that enforces machine learning good practice and enables clinicians and clinical researchers to rapidly and reproducibly discover metabolite patterns in diseases. We present BALSAM—a comprehensive web-platform to simplify and automate this process, offering features for preprocessing, peak detection, feature extraction, visualization and pattern discovery. Our main focus is on data from multi-capillary-column ion-mobility-spectrometry. While not limited to breath data, BALSAM was developed to increase consistency and robustness in the data analysis process of breath samples, aiming to expand the array of low cost molecular diagnostics in clinics. Our platform is freely available as a web-service and in form of a publicly available docker container.

scholarly journals NLOS Multipath Classification of GNSS Signal Correlation Output Using Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2503
Author(s):  
Taro Suzuki ◽  
Yoshiharu Amano
Keyword(s):  
Machine Learning ◽  
Satellite System ◽  
Training Data ◽  
Support Vector ◽  
Positioning Errors ◽  
Automated Method ◽  
Global Navigation Satellite ◽  
Better Than ◽  
Signal Correlation

This paper proposes a method for detecting non-line-of-sight (NLOS) multipath, which causes large positioning errors in a global navigation satellite system (GNSS). We use GNSS signal correlation output, which is the most primitive GNSS signal processing output, to detect NLOS multipath based on machine learning. The shape of the multi-correlator outputs is distorted due to the NLOS multipath. The features of the shape of the multi-correlator are used to discriminate the NLOS multipath. We implement two supervised learning methods, a support vector machine (SVM) and a neural network (NN), and compare their performance. In addition, we also propose an automated method of collecting training data for LOS and NLOS signals of machine learning. The evaluation of the proposed NLOS detection method in an urban environment confirmed that NN was better than SVM, and 97.7% of NLOS signals were correctly discriminated.

scholarly journals Deep Learning Methods for Classification of Certain Abnormalities in Echocardiography

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 495
Author(s):  
Imayanmosha Wahlang ◽  
Arnab Kumar Maji ◽  
Goutam Saha ◽  
Prasun Chakrabarti ◽  
Michal Jasinski ◽  
...  
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Support Vector ◽  
Variational Autoencoder ◽  
Different Types ◽  
Static Images ◽  
Long Short Term Memory ◽  
2D And 3D ◽  
Better Than

This article experiments with deep learning methodologies in echocardiogram (echo), a promising and vigorously researched technique in the preponderance field. This paper involves two different kinds of classification in the echo. Firstly, classification into normal (absence of abnormalities) or abnormal (presence of abnormalities) has been done, using 2D echo images, 3D Doppler images, and videographic images. Secondly, based on different types of regurgitation, namely, Mitral Regurgitation (MR), Aortic Regurgitation (AR), Tricuspid Regurgitation (TR), and a combination of the three types of regurgitation are classified using videographic echo images. Two deep-learning methodologies are used for these purposes, a Recurrent Neural Network (RNN) based methodology (Long Short Term Memory (LSTM)) and an Autoencoder based methodology (Variational AutoEncoder (VAE)). The use of videographic images distinguished this work from the existing work using SVM (Support Vector Machine) and also application of deep-learning methodologies is the first of many in this particular field. It was found that deep-learning methodologies perform better than SVM methodology in normal or abnormal classification. Overall, VAE performs better in 2D and 3D Doppler images (static images) while LSTM performs better in the case of videographic images.

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