Application of the synergetic algorithm on the classification of lymph tissue cells

Liu, Binghan; Liu, Yuhong; Zhang, Jing; Zeng, Yanjun; Wang, Weizhi

doi:10.1016/j.compbiomed.2008.03.003

Cited by 6 publications

(7 citation statements)

References 8 publications

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“…Other clustering algorithms have also been used to segment HIs. The work proposed by Liu et al [18] used the iterative self-organizing data analysis technique (ISODATA) to cluster cell images and create prototypes. Hafiane et al [19] studied two strategies for initialization of clustering methods: geodesic active contours and multi-phase vector level sets.…”

Section: Unsupervised Approachesmentioning

confidence: 99%

“…Year Tissue/Organ Method Liu et al [18] 2008 Lymph nodes ISODATA Tosun et al [13] 2009 Colorectal k-means Hafiane et al [19] 2009 Prostate Spatial constraint fuzzy c-means He et al [20] 2010 Cervix Gaussian mixture models Fatakdawala et al [5] 2010 Breast k-means Roullier et al [6] 2011 Breast k-means Rahmadwati et al [7] 2011 Uterus k-means Peng et al [8] 2011 Prostate k-means He et al [9] 2011 Uterus k-means Onder et al [21] 2013 Colorectal Quasi-supervised nearest neighbors Fatima et al [10] 2014 Brain k-means Nativ et al [14] 2014 Liver k-means Yang et al [22] 2014 Prostate Mean shift, Similarity Sirinukunwattana et al [23] 2015 Breast Dictionary, Thresholding Huang [24] 2015 Breast XCA Mazo et al [11] 2016 Cardiac k-means Shi et al [15] 2016 Lymph nodes k-means Brieu et al [16] 2016 Lung k-means Shi et al [17] 2017 Liver k-means Shi et al [15] 2017 Lymph nodes k-means…”

Section: Referencementioning

confidence: 99%

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Machine Learning Methods for Histopathological Image Analysis: A Review

et al. 2021

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Histopathological images (HIs) are the gold standard for evaluating some types of tumors for cancer diagnosis. The analysis of such images is time and resource-consuming and very challenging even for experienced pathologists, resulting in inter-observer and intra-observer disagreements. One of the ways of accelerating such an analysis is to use computer-aided diagnosis (CAD) systems. This paper presents a review on machine learning methods for histopathological image analysis, including shallow and deep learning methods. We also cover the most common tasks in HI analysis, such as segmentation and feature extraction. Besides, we present a list of publicly available and private datasets that have been used in HI research.

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Section: Unsupervised Approachesmentioning

confidence: 99%

Section: Referencementioning

confidence: 99%

Machine Learning Methods for Histopathological Image Analysis: A Review

et al. 2021

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“…Other clustering algorithms have also been used to segment HIs. The work proposed by Liu et al [19] used the iterative self-organizing data analysis technique (ISODATA) to cluster cell images and create prototypes. Hafiane et al [20] studied two strategies for initialization of clustering methods: geodesic active contours and multi-phase vector level sets.…”

Section: Unsupervised Approachesmentioning

confidence: 99%

Machine Learning Methods for Histopathological Image Analysis: A Review

Matos¹,

Ataky²,

Britto³

et al. 2021

Preprint

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Histopathological images (HIs) are the gold standard for evaluating some types of tumors for cancer diagnosis. The analysis of such images is not only time and resource consuming, but also very challenging even for experienced pathologists, resulting in inter-and intra-observer disagreements. One of the ways of accelerating such an analysis is to use computer-aided diagnosis (CAD) systems. In this paper, we present a review on machine learning methods for histopathological image analysis, including shallow and deep learning methods. We also cover the most common tasks in HI analysis, such as segmentation and feature extraction. In addition, we present a list of publicly available and private datasets that have been used in HI research.

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“…However these symptoms are not unique to tuberculosis, leading to possible misdiagnoses. One disease that is often misdiagnosed as TB is lymphoma, a cancer which occurs when lymphocytes inside the lymph nodes multiply too fast or live too long [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Classifying lymphoma and tuberculosis case reports using machine learning algorithms

et al. 2021

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Available literature reports several lymphoma cases misdiagnosed as tuberculosis, especially in countries with a heavy TB burden. This frequent misdiagnosis is due to the fact that the two diseases can present with similar symptoms. The present study therefore aims to analyse and explore TB as well as lymphoma case reports using Natural Language Processing tools and evaluate the use of machine learning to differentiate between the two diseases. As a starting point in the study, case reports were collected for each disease using web scraping. Natural language processing tools and text clustering were then used to explore the created dataset. Finally, six machine learning algorithms were trained and tested on the collected data, which contained 765 lymphoma and 546 tuberculosis case reports. Each method was evaluated using various performance metrics. The results indicated that the multi-layer perceptron model achieved the best accuracy (93.1%), recall (91.9%) and precision score (93.7%), thus outperforming other algorithms in terms of correctly classifying the different case reports.

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Application of the synergetic algorithm on the classification of lymph tissue cells

Cited by 6 publications

References 8 publications

Machine Learning Methods for Histopathological Image Analysis: A Review

Machine Learning Methods for Histopathological Image Analysis: A Review

Machine Learning Methods for Histopathological Image Analysis: A Review

Classifying lymphoma and tuberculosis case reports using machine learning algorithms

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