Diagnosis Approaches for Colorectal Cancer Using Manifold Learning and Deep Learning

Data visualization is still a challenge for numerous fields. For metagenomic data, datasets are usually characterized by very high-dimensional data which are hard to interpret to humans. Among diseases using metagenomic data for prediction, deep learning usually yields a lower performance comparing...

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Bibliographic Details
Published in:SN computer science 2020-09, Vol.1 (5), p.281, Article 281
Main Authors: Thanh-Hai, Nguyen, Thai-Nghe, Nguyen
Format: Article
Language:English
Subjects:
Algorithms
Cancer
Colorectal cancer
Computer Imaging
Computer Science
Computer Systems Organization and Communication Networks
Data Structures and Information Theory
Datasets
Deep learning
Diagnosis
Embedding
Information Systems and Communication Service
Machine learning
Manifolds (mathematics)
Methods
Original Research
Pattern Recognition and Graphics
Performance enhancement
Phylogenetics
Scientific visualization
Software
Software Engineering/Programming and Operating Systems
Software Technology and Its Enabling Computing Platforms
Taxonomy
Vision
Visualization
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Description
Summary:Data visualization is still a challenge for numerous fields. For metagenomic data, datasets are usually characterized by very high-dimensional data which are hard to interpret to humans. Among diseases using metagenomic data for prediction, deep learning usually yields a lower performance comparing to classical machine learning for colorectal cancer prediction. In this paper, we present an approach using manifold learning with t-distributed stochastic neighbor embedding (t-SNE) and spectral embedding to visualize numerical data into images and leverage deep learning algorithms to improve the performance in colorectal cancer diseases prediction. The work also provides promising potentials to improve the visualization quality and performance in prediction tasks on dense data. The analytical results of samples coming from five various regions including America, China, Austria, Germany, and France show promising in use of combination between these visualization approaches and deep learning to enhance the performance in colorectal cancer disease diagnosis.
ISSN:2662-995X
2661-8907
DOI:10.1007/s42979-020-00297-7