Classification of microscopic peripheral blood cell images using multibranch lightweight CNN-based model
| dc.authorid | 0000-0002-1257-8518 | en_US |
| dc.contributor.author | Fırat, Hüseyin | |
| dc.date.accessioned | 2024-03-11T11:45:14Z | |
| dc.date.available | 2024-03-11T11:45:14Z | |
| dc.date.issued | 2023 | en_US |
| dc.department | Dicle Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü | en_US |
| dc.description.abstract | White blood cells (WBC), which are human peripheral blood cells, are the most significant part of the immune system that defends the body against microorganisms. Modifications in the morphological structure and number of subtypes of WBC play an major role in the diagnosis of serious diseases such as anemia and leukemia. Therefore, accurate WBC classification is clinically quite significant in the diagnosis of the disease. In last years, deep learning, especially CNN, has been used frequently in the field of medicine because of its strong self-learning capabilities and it can extract deeper features in images with stronger semantic information. In this study, a new CNN-based method is proposed for WBC classification. The proposed method (PM) is a hybrid method consisting of Inception module, pyramid pooling module (PPM) and depthwise squeeze-and-excitation block (DSEB). Inception module increases classification accuracy of CNNs by performing multiple parallel convolutions at different scales. PPM captures multi-scale contextual information from the input image by pooling features at multiple different scales. DSEB offers a structure where the network can selectively learn about informative features and remove useless ones. For the analysis of the classification results of the PM, experiments were carried out on three different datasets consisting of four classes (BCCD dataset), five classes (Raabin WBC dataset) and eight classes. As a result of the experimental studies, classification accuracy was obtained 99.96% in the BCCD dataset containing 4 classes, 99.22% in the Raabin WBC dataset containing 5 classes and 99.72% in the PBC dataset containing 8 classes. Compared with the state-of-the-art studies in the literature, the PM achieved the best accuracy in three datasets. | en_US |
| dc.identifier.citation | Fırat, H. (2023). Classification of microscopic peripheral blood cell images using multibranch lightweight CNN-based model. Neural Computing and Applications, 36(4), 1599-1620. | en_US |
| dc.identifier.doi | 10.1007/s00521-023-09158-9 | |
| dc.identifier.endpage | 1620 | en_US |
| dc.identifier.issn | 0941-0643 | |
| dc.identifier.issue | 4 | en_US |
| dc.identifier.scopus | 2-s2.0-85176320896 | |
| dc.identifier.scopusquality | Q1 | |
| dc.identifier.startpage | 1599 | en_US |
| dc.identifier.uri | https://link.springer.com/article/10.1007/s00521-023-09158-9 | |
| dc.identifier.uri | https://hdl.handle.net/11468/13555 | |
| dc.identifier.volume | 36 | en_US |
| dc.identifier.wos | WOS:001100575600002 | |
| dc.identifier.wosquality | N/A | |
| dc.indekslendigikaynak | Web of Science | |
| dc.indekslendigikaynak | Scopus | |
| dc.institutionauthor | Fırat, Hüseyin | |
| dc.language.iso | en | en_US |
| dc.publisher | Springer Science and Business Media Deutschland GmbH | en_US |
| dc.relation.ispartof | Neural Computing and Applications | |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | Depthwise squeeze-and-excitation block | en_US |
| dc.subject | Multibranch lightweight CNN | en_US |
| dc.subject | Peripheral blood cell images | en_US |
| dc.subject | Pyramid pooling module | en_US |
| dc.title | Classification of microscopic peripheral blood cell images using multibranch lightweight CNN-based model | en_US |
| dc.title | Classification of microscopic peripheral blood cell images using multibranch lightweight CNN-based model | |
| dc.type | Article | en_US |
