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

1
Information Technology Department, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt
2
Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), Universidade de Santiago de Compostela, 15705 Santiago de Compostela, Spain
3
Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Benha 13512, Egypt
4
Department of Computer Science and Engineering, Sejong University, Seoul 05006, Korea
*
Author to whom correspondence should be addressed.
Mathematics 2020, 8(11), 1863; https://doi.org/10.3390/math8111863
Received: 21 September 2020 / Revised: 18 October 2020 / Accepted: 19 October 2020 / Published: 24 October 2020
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. View Full-Text
Keywords: medical image analysis; histopathology image analysis; conventional machine learning methods; deep learning methods; computer-assisted diagnosis medical image analysis; histopathology image analysis; conventional machine learning methods; deep learning methods; computer-assisted diagnosis
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MDPI and ACS Style

Elazab, N.; Soliman, H.; El-Sappagh, S.; Islam, S.M.R.; Elmogy, M. Objective Diagnosis for Histopathological Images Based on Machine Learning Techniques: Classical Approaches and New Trends. Mathematics 2020, 8, 1863. https://doi.org/10.3390/math8111863

AMA Style

Elazab N, Soliman H, El-Sappagh S, Islam SMR, Elmogy M. Objective Diagnosis for Histopathological Images Based on Machine Learning Techniques: Classical Approaches and New Trends. Mathematics. 2020; 8(11):1863. https://doi.org/10.3390/math8111863

Chicago/Turabian Style

Elazab, Naira; Soliman, Hassan; El-Sappagh, Shaker; Islam, S. M.R.; Elmogy, Mohammed. 2020. "Objective Diagnosis for Histopathological Images Based on Machine Learning Techniques: Classical Approaches and New Trends" Mathematics 8, no. 11: 1863. https://doi.org/10.3390/math8111863

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