scholarly journals Deep-learning in-situ classification of HIV-1 virion morphology

2021 ◽  
Author(s):  
Juan S. Rey ◽  
Wen Li ◽  
Alexander J. Bryer ◽  
Hagan Beatson ◽  
Christian Lantz ◽  
...  
Keyword(s):  
Deep Learning ◽  
Hiv 1

scholarly journals Interpretability of a Deep Learning Based Approach for the Classification of Skin Lesions into Main Anatomic Body Sites

Cancers ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 6048
Author(s):  
Joanna Jaworek-Korjakowska ◽  
Andrzej Brodzicki ◽  
Bill Cassidy ◽  
Connah Kendrick ◽  
Moi Hoon Yap
Keyword(s):  
Deep Learning ◽  
Malignant Melanoma ◽  
Skin Lesions ◽  
The Body ◽  
Anatomic Site ◽  
The Past ◽  
Depth Analysis ◽  
The Face

Over the past few decades, different clinical diagnostic algorithms have been proposed to diagnose malignant melanoma in its early stages. Furthermore, the detection of skin moles driven by current deep learning based approaches yields impressive results in the classification of malignant melanoma. However, in all these approaches, the researchers do not take into account the origin of the skin lesion. It has been observed that the specific criteria for in situ and early invasive melanoma highly depend on the anatomic site of the body. To address this problem, we propose a deep learning architecture based framework to classify skin lesions into the three most important anatomic sites, including the face, trunk and extremities, and acral lesions. In this study, we take advantage of pretrained networks, including VGG19, ResNet50, Xception, DenseNet121, and EfficientNetB0, to calculate the features with an adjusted and densely connected classifier. Furthermore, we perform in depth analysis on database, architecture, and result regarding the effectiveness of the proposed framework. Experiments confirm the ability of the developed algorithms to classify skin lesions into the most important anatomical sites with 91.45% overall accuracy for the EfficientNetB0 architecture, which is a state-of-the-art result in this domain.

scholarly journals Classification of Seismic Events with Deep Learning Strategies: Insights from the Moosfluh Landslide

2020 ◽  
Author(s):  
Nadir Dazzi ◽  
Andrea Manconi ◽  
Nikhil Prakash ◽  
Valentin Bickel
Keyword(s):  
Deep Learning ◽  
Learning Strategies ◽  
Seismic Data ◽  
Low Cost ◽  
Sampling Rate ◽  
Seismic Signals ◽  
Processing Strategies ◽  
Steep Slopes

<p>Rockfalls affect steep slopes in several geographic regions. Different systems from remote to in-situ instruments are used for their detection and study. In this scenario, seismic signals produced by the detachment, bouncing, and rolling of rockfalls are being increasingly used for the detection and classification of such events. This is typically done by using different manual, semi-automatic and/or automatic signal processing strategies. In this work, we applied a new Deep Learning (DL) algorithm in order to test the performance on the automatic classification of seismic signals. We applied the method to seismic data acquired by a low-cost Raspberry Shake 1D seismometer (sampling rate 50Hz) in order to discriminate rockfall from not-rockfall events occurred at the Moosfluh active slope region in Wallis (CH). Here we present the methodology and show the results obtained on a continuous record of more than 2-years of seismic data. The performance accuracy of the DL approach reached values larger than 90%. Our results show that the application of DL strategies in this context can be very useful and save time on seismic data classification.</p>

scholarly journals In situ classification of cell types in human kidney tissue using 3D nuclear staining

2020 ◽  
Author(s):  
Andre Woloshuk ◽  
Suraj Khochare ◽  
Aljohara Fahad Almulhim ◽  
Andrew McNutt ◽  
Dawson Dean ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Kidney Tissue ◽  
Human Kidney ◽  
Cell Types ◽  
Learning Approach ◽  
Cell Type ◽  
Cell Classification

AbstractTo understand the physiology and pathology of disease, capturing the heterogeneity of cell types within their tissue environment is fundamental. In such an endeavor, the human kidney presents a formidable challenge because its complex organizational structure is tightly linked to key physiological functions. Advances in imaging-based cell classification may be limited by the need to incorporate specific markers that can link classification to function. Multiplex imaging can mitigate these limitations, but requires cumulative incorporation of markers, which may lead to tissue exhaustion. Furthermore, the application of such strategies in large scale 3-dimensional (3D) imaging is challenging. Here, we propose that 3D nuclear signatures from a DNA stain, DAPI, which could be incorporated in most experimental imaging, can be used for classifying cells in intact human kidney tissue. We developed an unsupervised approach that uses 3D tissue cytometry to generate a large training dataset of nuclei images (NephNuc), where each nucleus is associated with a cell type label. We then devised various supervised machine learning approaches for kidney cell classification and demonstrated that a deep learning approach outperforms classical machine learning or shape-based classifiers. Specifically, a custom 3D convolutional neural network (NephNet3D) trained on nuclei image volumes achieved a balanced accuracy of 80.26%. Importantly, integrating NephNet3D classification with tissue cytometry allowed in situ visualization of cell type classifications in kidney tissue. In conclusion, we present a tissue cytometry and deep learning approach for in situ classification of cell types in human kidney tissue using only a DNA stain. This methodology is generalizable to other tissues and has potential advantages on tissue economy and non-exhaustive classification of different cell types.

Classification of Customer to Upgrade Profits in Retail Market with Deep Learning Methodology

2019 ◽  
Vol 7 (5) ◽  
pp. 188-191
Author(s):  
I.Gayathri Devi ◽  
G. Surya Kala Eswari ◽  
G. Kumari
Keyword(s):  
Deep Learning ◽  
Retail Market

Deep Learning through Convolutional Neural Networks for Classification of Image A Novel Approach Using Hyper Filter

2019 ◽  
Vol 7 (6) ◽  
pp. 164-168
Author(s):  
Kshitij Tripathi ◽  
Rajendra G. Vyas ◽  
Anil K. Gupta
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Novel Approach

A Study on the Auxiliary Diagnosis of Thyroid Disease Images Based on Multiple Dimensional Deep Learning Algorithms

2020 ◽  
Vol 16 (3) ◽  
pp. 199-205
Author(s):  
Yuejun Liu ◽  
Yifei Xu ◽  
Xiangzheng Meng ◽  
Xuguang Wang ◽  
Tianxu Bai
Keyword(s):  
Deep Learning ◽  
Learning Algorithms ◽  
Region Of Interest ◽  
Classification Performance ◽  
Thyroid Diseases ◽  
Great Success ◽  
Learning Models ◽  
Good Classification Performance ◽  
Spect Images

Background: Medical imaging plays an important role in the diagnosis of thyroid diseases. In the field of machine learning, multiple dimensional deep learning algorithms are widely used in image classification and recognition, and have achieved great success. Objective: The method based on multiple dimensional deep learning is employed for the auxiliary diagnosis of thyroid diseases based on SPECT images. The performances of different deep learning models are evaluated and compared. Methods: Thyroid SPECT images are collected with three types, they are hyperthyroidism, normal and hypothyroidism. In the pre-processing, the region of interest of thyroid is segmented and the amount of data sample is expanded. Four CNN models, including CNN, Inception, VGG16 and RNN, are used to evaluate deep learning methods. Results: Deep learning based methods have good classification performance, the accuracy is 92.9%-96.2%, AUC is 97.8%-99.6%. VGG16 model has the best performance, the accuracy is 96.2% and AUC is 99.6%. Especially, the VGG16 model with a changing learning rate works best. Conclusion: The standard CNN, Inception, VGG16, and RNN four deep learning models are efficient for the classification of thyroid diseases with SPECT images. The accuracy of the assisted diagnostic method based on deep learning is higher than that of other methods reported in the literature.

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