Objective Diagnosis for Histopathological Images Based on Machine Learning Techniques: Classical Approaches and New Trends

Naira Elazab, Hassan Soliman, Shaker El-Sappagh, S. M. Riazul Islam and Mohammed Elmogy
Additional contact information
Naira Elazab: Information Technology Department, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt
Hassan Soliman: Information Technology Department, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt
Shaker El-Sappagh: Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), Universidade de Santiago de Compostela, 15705 Santiago de Compostela, Spain
S. M. Riazul Islam: Department of Computer Science and Engineering, Sejong University, Seoul 05006, Korea
Mohammed Elmogy: Information Technology Department, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt

Mathematics, 2020, vol. 8, issue 11, 1-26

Abstract: Histopathology refers to the examination by a pathologist of biopsy samples. Histopathology images are captured by a microscope to locate, examine, and classify many diseases, such as different cancer types. They provide a detailed view of different types of diseases and their tissue status. These images are an essential resource with which to define biological compositions or analyze cell and tissue structures. This imaging modality is very important for diagnostic applications. The analysis of histopathology images is a prolific and relevant research area supporting disease diagnosis. In this paper, the challenges of histopathology image analysis are evaluated. An extensive review of conventional and deep learning techniques which have been applied in histological image analyses is presented. This review summarizes many current datasets and highlights important challenges and constraints with recent deep learning techniques, alongside possible future research avenues. Despite the progress made in this research area so far, it is still a significant area of open research because of the variety of imaging techniques and disease-specific characteristics.

Keywords: medical image analysis; histopathology image analysis; conventional machine learning methods; deep learning methods; computer-assisted diagnosis (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/8/11/1863/pdf (application/pdf)
https://www.mdpi.com/2227-7390/8/11/1863/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:8:y:2020:i:11:p:1863-:d:434304

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().