scholarly journals Protein Family Classification using Deep Learning

2018 ◽  
Author(s):  
K S Naveenkumar ◽  
Babu R Mohammed Harun ◽  
R Vinayakumar ◽  
KP Soman
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Protein Classification ◽  
Biological Sequences ◽  
Biological Sequence ◽  
Deep Learning Algorithm ◽  
N Gram ◽  
Family Classification ◽  
Selection Of

AbstractProtein classification is responsible for the biological sequence, we came up with an idea which deals with the classification of proteomics using deep learning algorithm. This algorithm focuses mainly to classify sequences of protein-vector which is used for the representation of proteomics. Selection of the type protein representation is challenging based on which output in terms of accuracy is depended on, The protein representation used here is n-gram i.e. 3-gram and Keras embedding used for biological sequences like protein. In this paper we are working on the Protein classification to show the strength and representation of biological sequence of the proteins.

scholarly journals Automated Detection and Classification of Desmoplastic Reaction at the Colorectal Tumour Front Using Deep Learning

Cancers ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1615
Author(s):  
Ines P. Nearchou ◽  
Hideki Ueno ◽  
Yoshiki Kajiwara ◽  
Kate Lillard ◽  
Satsuki Mochizuki ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Prognostic Significance ◽  
Stage Ii ◽  
Full Cohort ◽  
Test Set ◽  
Colorectal Tumour ◽  
Desmoplastic Reaction ◽  
Deep Learning Algorithm

The categorisation of desmoplastic reaction (DR) present at the colorectal cancer (CRC) invasive front into mature, intermediate or immature type has been previously shown to have high prognostic significance. However, the lack of an objective and reproducible assessment methodology for the assessment of DR has been a major hurdle to its clinical translation. In this study, a deep learning algorithm was trained to automatically classify immature DR on haematoxylin and eosin digitised slides of stage II and III CRC cases (n = 41). When assessing the classifier’s performance on a test set of patient samples (n = 40), a Dice score of 0.87 for the segmentation of myxoid stroma was reported. The classifier was then applied to the full cohort of 528 stage II and III CRC cases, which was then divided into a training (n = 396) and a test set (n = 132). Automatically classed DR was shown to have superior prognostic significance over the manually classed DR in both the training and test cohorts. The findings demonstrated that deep learning algorithms could be applied to assist pathologists in the detection and classification of DR in CRC in an objective, standardised and reproducible manner.

Classification of radioxenon spectra with deep learning algorithm

2021 ◽  
Vol 237 ◽  
pp. 106718
Author(s):  
Sepideh Alsadat Azimi ◽  
Hossein Afarideh ◽  
Jong-Seo Chai ◽  
Martin Kalinowski ◽  
Abdelhakim Gheddou ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Deep Learning Algorithm

Deep learning algorithm for the classification of spirometries using flow-volume curves: proof of concept study

2021 ◽  
Author(s):  
Konstantinos Exarchos ◽  
Dimitrios Potonos ◽  
Agapi Aggelopoulou ◽  
Agni Sioutkou ◽  
Konstantinos Kostikas
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Flow Volume ◽  
Proof Of Concept ◽  
Concept Study ◽  
Deep Learning Algorithm

YOLOv4 Based Deep Learning Algorithm for Defects Detection and Classification of Rail Surfaces

2021 ◽  
Author(s):  
Noreen Anwar ◽  
Zhen Shen ◽  
Qinglai Wei ◽  
Gang Xiong ◽  
Peijun Ye ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Deep Learning Algorithm

scholarly journals Classification of Shellfish Recognition Based on Improved Faster R-CNN Framework of Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yiran Feng ◽  
Xueheng Tao ◽  
Eung-Joo Lee
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Experimental Tests ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Multiple Objects ◽  
Detection Model ◽  
Deep Learning Algorithm ◽  
Quality Sorting

In view of the current absence of any deep learning algorithm for shellfish identification in real contexts, an improved Faster R-CNN-based detection algorithm is proposed in this paper. It achieves multiobject recognition and localization through a second-order detection network and replaces the original feature extraction module with DenseNet, which can fuse multilevel feature information, increase network depth, and avoid the disappearance of network gradients. Meanwhile, the proposal merging strategy is improved with Soft-NMS, where an attenuation function is designed to replace the conventional NMS algorithm, thereby avoiding missed detection of adjacent or overlapping objects and enhancing the network detection accuracy under multiple objects. By constructing a real contexts shellfish dataset and conducting experimental tests on a vision recognition seafood sorting robot production line, we were able to detect the features of shellfish in different scenarios, and the detection accuracy was improved by nearly 4% compared to the original detection model, achieving a better detection accuracy. This provides favorable technical support for future quality sorting of seafood using the improved Faster R-CNN-based approach.

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