A review for cervical histopathology image analysis using machine vision approaches

Li, Chen; Chen, Hao; Li, Xiaoyan; Xu, Ning; Hu, Zhijie; Dong, Xue; Qi, Shouliang; Ma, He; Zhang, Le; Sun, Hongzan

doi:10.1007/s10462-020-09808-7

Cited by 69 publications

(34 citation statements)

References 82 publications

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“…Histopathology is a significant branch of biology that covers the investigation of the cell anatomy and tissues of organisms at a microscopic level by a histopathologist [ 45 ]. Histopathological images are very influential for the final decision procedure of effective therapeutics; these images are essential to investigate the status of a certain biological structure and to diagnose diseases like cancer [ 39 , 45 ]. Digital histopathology represents a significant evolution in modern medicine [ 46 ].…”

Section: Histopathology Images Backgroundmentioning

confidence: 99%

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Role of AI and Histopathological Images in Detecting Prostate Cancer: A Survey

Ayyad

Shehata

Shalaby

et al. 2021

Sensors

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Prostate cancer is one of the most identified cancers and second most prevalent among cancer-related deaths of men worldwide. Early diagnosis and treatment are substantial to stop or handle the increase and spread of cancer cells in the body. Histopathological image diagnosis is a gold standard for detecting prostate cancer as it has different visual characteristics but interpreting those type of images needs a high level of expertise and takes too much time. One of the ways to accelerate such an analysis is by employing artificial intelligence (AI) through the use of computer-aided diagnosis (CAD) systems. The recent developments in artificial intelligence along with its sub-fields of conventional machine learning and deep learning provide new insights to clinicians and researchers, and an abundance of research is presented specifically for histopathology images tailored for prostate cancer. However, there is a lack of comprehensive surveys that focus on prostate cancer using histopathology images. In this paper, we provide a very comprehensive review of most, if not all, studies that handled the prostate cancer diagnosis using histopathological images. The survey begins with an overview of histopathological image preparation and its challenges. We also briefly review the computing techniques that are commonly applied in image processing, segmentation, feature selection, and classification that can help in detecting prostate malignancies in histopathological images.

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Section: Histopathology Images Backgroundmentioning

confidence: 99%

“…Stages of the preparation process of the tissue slides are as presented in Figure 3 . It consists of five operations, and each of them can affect the quality of the final image [ 38 , 45 ]. (I) Fixation: Samples of biological tissues are fixed with chemical fixation.…”

Section: Histopathology Images Backgroundmentioning

confidence: 99%

Role of AI and Histopathological Images in Detecting Prostate Cancer: A Survey

Ayyad

Shehata

Shalaby

et al. 2021

Sensors

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“…In [29], we utilize graph and unsupervised learning methods in a tissue structure clustering task, and divide the histopathological images of cervical cancer into sparse areas and viscose areas to predict the risk of the tissues. For more information, please refer to our previous survey papers [30].…”

Section: B Relevant Classical Work In the Cad Fieldmentioning

confidence: 99%

An Application of Transfer Learning and Ensemble Learning Techniques for Cervical Histopathology Image Classification

et al. 2020

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In recent years, researches are concentrating on the effectiveness of Transfer Learning (TL) and Ensemble Learning (EL) techniques in cervical histopathology image analysis. However, there have been very few investigations that have described the stages of differentiation of cervical histopathological images. Therefore, in this article, we propose an Ensembled Transfer Learning (ETL) framework to classify well, moderate and poorly differentiated cervical histopathological images. First of all, we have developed Inception-V3, Xception, VGG-16, and Resnet-50 based TL structures. Then, to enhance the classification performance, a weighted voting based EL strategy is introduced. After that, to evaluate the proposed algorithm, a dataset consisting of 307 images, stained by three immunohistochemistry methods (AQP, HIF, and VEGF) is considered. In the experiment, we obtain the highest overall accuracy of 97.03% and 98.61% on AQP staining images and poor differentiation of VEGF staining images, individually. Finally, an additional experiment for classifying the benign cells from the malignant ones is carried out on the Herlev dataset and obtains an overall accuracy of 98.37%.

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“…Cytology and histology both are the examinations of cells at a microscopic level. Histology examination is usually performed by investigating the sample of whole tissue, whereas cytology is the examination of cell samples [41]- [43]. The preparation of histology slides involves lots of stages compared to the preparation of cytology slides, which makes it costlier.…”

Section: Motivation Of Our Review Papermentioning

confidence: 99%

A Survey for Cervical Cytopathology Image Analysis Using Deep Learning

Rahaman

et al. 2020

IEEE Access

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102

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Cervical cancer is one of the most common and deadliest cancers among women. Despite that, this cancer is entirely treatable if it is detected at a precancerous stage. Pap smear test is the most extensively performed screening method for early detection of cervical cancer. However, this hand-operated screening approach suffers from a high false-positive result because of human errors. To improve the accuracy and manual screening practice, computer-aided diagnosis methods based on deep learning is developed widely to segment and classify the cervical cytology images automatically. In this survey, we provide a comprehensive study of the state of the art approaches based on deep learning for the analysis of cervical cytology images. Firstly, we introduce deep learning and its simplified architectures that have been used in this field. Secondly, we discuss the publicly available cervical cytopathology datasets and evaluation metrics for segmentation and classification tasks. Then, a thorough review of the recent development of deep learning for the segmentation and classification of cervical cytology images is presented. Finally, we investigate the existing methodology along with the most suitable techniques for the analysis of pap smear cells.

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A review for cervical histopathology image analysis using machine vision approaches

Cited by 69 publications

References 82 publications

Role of AI and Histopathological Images in Detecting Prostate Cancer: A Survey

Role of AI and Histopathological Images in Detecting Prostate Cancer: A Survey

An Application of Transfer Learning and Ensemble Learning Techniques for Cervical Histopathology Image Classification

A Survey for Cervical Cytopathology Image Analysis Using Deep Learning

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