Rapid Automated Classification of Anesthetic Depth Levels using GPU Based Parallelization of Neural Networks

AbstractThe need for a fast and robust method to characterize nanostructure thickness is growing due to the tremendous number of experiments and their associated applications. By automatically analyzing the microscopic image texture of MoS2 and WS2, it was possible to distinguish monolayer from few-layer nanostructures with high accuracy for both materials. Three methods of texture analysis (TA) were used: grey level histogram (GLH), grey levels co-occurrence matrix (GLCOM), and run-length matrix (RLM), which correspond to first, second, and higher-order statistical methods, respectively. The best discriminating features were automatically selected using the Fisher coefficient, for each method, and used as a base for classification. Two classifiers were used: artificial neural networks (ANN), and linear discriminant analysis (LDA). RLM with ANN was found to give high classification accuracy, which was 89% and 95% for MoS2 and WS2, respectively. The result of this work suggests that RLM, as a higher-order TA method, associated with an ANN classifier has a better ability to quantify and characterize the microscopic structure of nanolayers, and, therefore, categorize thickness to the proper class.

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Towards Designing an Automated Classification of Lymphoma subtypes using Deep Neural Networks

Proceedings of the ACM India Joint International Conference on Data Science and Management of Data - CoDS-COMAD '19 ◽

10.1145/3297001.3297019 ◽

2019 ◽

Cited By ~ 1

Author(s):

Rucha Tambe ◽

Sarang Mahajan ◽

Urmil Shah ◽

Mohit Agrawal ◽

Bhushan Garware

Keyword(s):

Neural Networks ◽

Deep Neural Networks ◽

Automated Classification

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A comprehensive study on classification of COVID-19 on computed tomography with pretrained convolutional neural networks

Scientific Reports ◽

10.1038/s41598-020-74164-z ◽

2020 ◽

Vol 10 (1) ◽

Cited By ~ 2

Author(s):

Tuan D. Pham

Keyword(s):

Computed Tomography ◽

Neural Networks ◽

Convolutional Neural Networks ◽

High Performance ◽

Data Augmentation ◽

Ct Scans ◽

Automated Classification ◽

Imaging Data ◽

Use Of Data

Abstract The use of imaging data has been reported to be useful for rapid diagnosis of COVID-19. Although computed tomography (CT) scans show a variety of signs caused by the viral infection, given a large amount of images, these visual features are difficult and can take a long time to be recognized by radiologists. Artificial intelligence methods for automated classification of COVID-19 on CT scans have been found to be very promising. However, current investigation of pretrained convolutional neural networks (CNNs) for COVID-19 diagnosis using CT data is limited. This study presents an investigation on 16 pretrained CNNs for classification of COVID-19 using a large public database of CT scans collected from COVID-19 patients and non-COVID-19 subjects. The results show that, using only 6 epochs for training, the CNNs achieved very high performance on the classification task. Among the 16 CNNs, DenseNet-201, which is the deepest net, is the best in terms of accuracy, balance between sensitivity and specificity, $$F_1$$ F 1 score, and area under curve. Furthermore, the implementation of transfer learning with the direct input of whole image slices and without the use of data augmentation provided better classification rates than the use of data augmentation. Such a finding alleviates the task of data augmentation and manual extraction of regions of interest on CT images, which are adopted by current implementation of deep-learning models for COVID-19 classification.

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