Back Propagation Neural Network in the Discrimination of Benign From Malignant Lower Urinary Tract Lesions

Pantazopoulos, D; Karakitsos, Petros; Iokim-Liossi, Anna; Pouliakis, Abraham; Botsoli-Stergiou, Efi; Dimopoulos, C.

doi:10.1097/00005392-199805000-00057

Cited by 48 publications

(23 citation statements)

References 12 publications

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“…42,No 7 In this study, we tried to reduce the subjectivity introduced by humans and to introduce a more objective methodology. Specifically, we used objective and reproducible nuclear measurements, thus each nucleus is represented by a set of numbers.…”

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confidence: 99%

Using classification and regression trees, liquid‐based cytology and nuclear morphometry for the discrimination of endometrial lesions

Pouliakis

Margari

et al. 2013

Diagnostic Cytopathology

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'The objective of this study is to investigate the potential of classification and regression trees (CARTs) in discriminating benign from malignant endometrial nuclei and lesions. The study was performed on 222 histologically confirmed liquid based cytological smears, specifically: 117 benign cases, 62 malignant cases and 43 hyperplasias with or without atypia. About 100 nuclei were measured from each case using an image analysis system; in total, we collected 22783 nuclei. The nuclei from 50% of the cases (the training set) were used to construct a CART model that was used for knowledge extraction. The nuclei from the remaining 50% of cases (test set) were used to evaluate the stability and performance of the CART on unknown data. Based on the results of the CART for nuclei classification, we propose two classification methods to discriminate benign from malignant cases. The CART model had an overall accuracy for the classification of endometrial nuclei equal to 85%, specificity 90.68%, and sensitivity 72.05%. Both methods for case classification had similar performance: overall accuracy in the range 94-95%, specificity 95%, and sensitivity 91-94%. The results of the proposed system outperform the standard cytological diagnosis of endometrial lesions. This study highlights interesting diagnostic features of endometrial nuclear morphology and provides a new classification approach for endometrial nuclei and cases. The proposed method can be a useful tool for the everyday practice of the cytological laboratory.

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confidence: 99%

Using classification and regression trees, liquid‐based cytology and nuclear morphometry for the discrimination of endometrial lesions

Pouliakis

Margari

et al. 2013

Diagnostic Cytopathology

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“…Its implementation in endometrial cytological analysis appears to help overcoming the subjective nature of the standard evaluation, which is related to the lack of consensus in the measuring process, resulting in poor reproducibility, and significant interobserver and intraobserver variability . To achieve this, various techniques have been tested so far, involving either classical statistical models or more advanced techniques, such as neural networks …”

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confidence: 99%

Image analysis and multi‐layer perceptron artificial neural networks for the discrimination between benign and malignant endometrial lesions

Makris

Pouliakis

Siristatidis

et al. 2017

Diagnostic Cytopathology

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“…ANNs are applicable in many areas as well as in the decision and classification problems found in medical and biomedical fields, such as the diagnosis of various diseases (including hypertension, cancer, rheumatic diseases, and vertigo), the analysis of medical images obtained by MRI and X-rays, and the prediction of a history of a disease [9]. Similarly, an ANN is the frequently used method in the diagnosis of prostate cancer [10]- [16], detecting changes in tumor and cell structures [17]- [22], tracking periodic differences in retinal images [23] and various urological dysfunctions [16], [24]- [28].…”

Section: Related Workmentioning

confidence: 99%

Estimating the Semen Quality from Life Style Using Fuzzy Radial Basis Functions

Candemir¹

2018

IJMLC

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Abstract-Infertility is a major problem that directly affects many people worldwide. In recent years, fertility rates have decreased by up to 15% in young men. Changing living conditions, stress levels, environmental factors and nutritional habits play an important role in infertility. This paper suggests a novel method to estimate semen quality from lifestyle, environmental factors and daily habits using radial basis function neural networks. The accuracy of the suggested method was measured as 90%. The results show that the proposed method can be applied as a practical, fast and cheap alternative method to laboratory tests.

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Back Propagation Neural Network in the Discrimination of Benign From Malignant Lower Urinary Tract Lesions

Abstract: The use of neural nets and image morphometry may increase the speed of cytological diagnosis and the diagnostic accuracy of voided urine cytology.

Cited by 48 publications

References 12 publications

Using classification and regression trees, liquid‐based cytology and nuclear morphometry for the discrimination of endometrial lesions

Using classification and regression trees, liquid‐based cytology and nuclear morphometry for the discrimination of endometrial lesions

Image analysis and multi‐layer perceptron artificial neural networks for the discrimination between benign and malignant endometrial lesions

Estimating the Semen Quality from Life Style Using Fuzzy Radial Basis Functions

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