Application of Neural Networks to the Classification of Pancreatic Intraductal Proliferative Lesions
The aim of the study was to test applycability of neural networks to classification of pancreatic intraductal proliferative lesions basing on nuclear features, especially chromatin texture. Material for the study was obtained from patients operated on for pancreatic cancer, chronic pancreatitis and...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2001-01-01
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| Series: | Analytical Cellular Pathology |
| Online Access: | http://dx.doi.org/10.1155/2001/657268 |
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| _version_ | 1849307333335711744 |
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| author | Krzysztof Okoń Romana Tomaszewska Krystyna Nowak Jerzy Stachura |
| author_facet | Krzysztof Okoń Romana Tomaszewska Krystyna Nowak Jerzy Stachura |
| author_sort | Krzysztof Okoń |
| collection | DOAJ |
| description | The aim of the study was to test applycability of neural networks to classification of pancreatic intraductal proliferative lesions basing on nuclear features, especially chromatin texture. Material for the study was obtained from patients operated on for pancreatic cancer, chronic pancreatitis and other tumours requiring pancreatic resection. Intraductal lesions were classified as low and high grade as previously described. The image analysis system consisted of a microscope, CCD camera combined with a PC and AnalySIS v. 2.11 software. The following texture characteristics were measured: variance of grey levels, features extracted from the grey levels correlation matrix and mean values, variance and standard deviation of the energy obtained from Laws matrices. Furthermore we used moments derived invariants and basic geometric data such as surface area, the minimum and maximum diameter and shape factor. The sets of data were randomly divided into training and testing groups. The training of the network using the back‐propagation algorithm, and the final classification of data was carried out with a neural network simulator SNNS v. 4.1. We studied the efficacy of networks containing from one to three hidden layers. Using the best network, containing three hidden layers, the rate of correct classification of nuclei was 73%, and the rate of misdiagnosis was 3%; in 24% the network response was ambiguous. The present findings may serve as a starting point in search for methods facilitating early diagnosis of ductal pancreatic carcinoma. |
| format | Article |
| id | doaj-art-2676e6b089724315abfc8cb4a9ec15ed |
| institution | Kabale University |
| issn | 0921-8912 1878-3651 |
| language | English |
| publishDate | 2001-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Analytical Cellular Pathology |
| spelling | doaj-art-2676e6b089724315abfc8cb4a9ec15ed2025-08-20T03:54:48ZengWileyAnalytical Cellular Pathology0921-89121878-36512001-01-01233-412913610.1155/2001/657268Application of Neural Networks to the Classification of Pancreatic Intraductal Proliferative LesionsKrzysztof Okoń0Romana Tomaszewska1Krystyna Nowak2Jerzy Stachura3Chair of Pathomorphology Jagiellonian University, Kraków, PolandChair of Pathomorphology Jagiellonian University, Kraków, Poland1st Chair of Surgery, Collegium Medicum, Jagiellonian University, Kraków, PolandChair of Pathomorphology Jagiellonian University, Kraków, PolandThe aim of the study was to test applycability of neural networks to classification of pancreatic intraductal proliferative lesions basing on nuclear features, especially chromatin texture. Material for the study was obtained from patients operated on for pancreatic cancer, chronic pancreatitis and other tumours requiring pancreatic resection. Intraductal lesions were classified as low and high grade as previously described. The image analysis system consisted of a microscope, CCD camera combined with a PC and AnalySIS v. 2.11 software. The following texture characteristics were measured: variance of grey levels, features extracted from the grey levels correlation matrix and mean values, variance and standard deviation of the energy obtained from Laws matrices. Furthermore we used moments derived invariants and basic geometric data such as surface area, the minimum and maximum diameter and shape factor. The sets of data were randomly divided into training and testing groups. The training of the network using the back‐propagation algorithm, and the final classification of data was carried out with a neural network simulator SNNS v. 4.1. We studied the efficacy of networks containing from one to three hidden layers. Using the best network, containing three hidden layers, the rate of correct classification of nuclei was 73%, and the rate of misdiagnosis was 3%; in 24% the network response was ambiguous. The present findings may serve as a starting point in search for methods facilitating early diagnosis of ductal pancreatic carcinoma.http://dx.doi.org/10.1155/2001/657268 |
| spellingShingle | Krzysztof Okoń Romana Tomaszewska Krystyna Nowak Jerzy Stachura Application of Neural Networks to the Classification of Pancreatic Intraductal Proliferative Lesions Analytical Cellular Pathology |
| title | Application of Neural Networks to the Classification of Pancreatic Intraductal Proliferative Lesions |
| title_full | Application of Neural Networks to the Classification of Pancreatic Intraductal Proliferative Lesions |
| title_fullStr | Application of Neural Networks to the Classification of Pancreatic Intraductal Proliferative Lesions |
| title_full_unstemmed | Application of Neural Networks to the Classification of Pancreatic Intraductal Proliferative Lesions |
| title_short | Application of Neural Networks to the Classification of Pancreatic Intraductal Proliferative Lesions |
| title_sort | application of neural networks to the classification of pancreatic intraductal proliferative lesions |
| url | http://dx.doi.org/10.1155/2001/657268 |
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