Effects of Histopathological Image Pre-processing on Convolutional Neural Networks
| dc.contributor.author | Öztürk, Şaban | |
| dc.contributor.author | Akdemir, Bayram | |
| dc.date.accessioned | 2020-03-26T20:11:45Z | |
| dc.date.available | 2020-03-26T20:11:45Z | |
| dc.date.issued | 2018 | |
| dc.department | Selçuk Üniversitesi | en_US |
| dc.description | 2018 International Conference on Computational Intelligence and Data Science, ICCIDS 2018 -- 7 April 2018 through 8 April 2018 -- 137053 | en_US |
| dc.description.abstract | In this study, classification performance of histopathological images which are processed by pre-processing algorithms using convolutional neural network structure is examined. The images are divided into four different pre-processing classes with their original state and processed with three different techniques. These classes are; original, normal pre-processing, other normal pre-processing and over pre-processing. Histopathological images of these four classes include cancerous and non-cancerous image patches. For these image classes, cancer patch classification is done using the same convolutional neural network structure. In this view, pre-processing effects on the classification success of the convolutional neural network is examined. For the normal pre-processing algorithm, background noise reduction and cell enhancement are applied. For over pre-processing, thresholding and morphological operations are applied in addition to normal preprocessing operations. At the end of the experiments, the most successful classification results are produced with the normal pre-processing algorithms. This is why the meaningful features of the image are left for the CNN structure that automatically learns the feature. The over pre-processing algorithm removes most of these important features from the image. © 2018 The Authors. Published by Elsevier Ltd. | en_US |
| dc.identifier.doi | 10.1016/j.procs.2018.05.166 | en_US |
| dc.identifier.endpage | 403 | en_US |
| dc.identifier.issn | 1877-0509 | en_US |
| dc.identifier.scopusquality | N/A | en_US |
| dc.identifier.startpage | 396 | en_US |
| dc.identifier.uri | https://dx.doi.org/10.1016/j.procs.2018.05.166 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12395/37194 | |
| dc.identifier.volume | 132 | en_US |
| dc.indekslendigikaynak | Scopus | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Elsevier B.V. | en_US |
| dc.relation.ispartof | Procedia Computer Science | en_US |
| dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.selcuk | 20240510_oaig | en_US |
| dc.subject | classification | en_US |
| dc.subject | CNN | en_US |
| dc.subject | convolutional neural networks | en_US |
| dc.subject | histopathological image | en_US |
| dc.subject | preprocessing | en_US |
| dc.title | Effects of Histopathological Image Pre-processing on Convolutional Neural Networks | en_US |
| dc.type | Conference Object | en_US |
