Histological tissue classification with a novel statistical filter-based convolutional neural network

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dc.authorid0000-0002-8107-4882
dc.contributor.authorUnlukal, Nejat
dc.contributor.authorUlker, Erkan
dc.contributor.authorSolmaz, Merve
dc.contributor.authorUyar, Kubra
dc.contributor.authorTasdemir, Sakir
dc.date.accessioned2026-01-24T12:29:01Z
dc.date.available2026-01-24T12:29:01Z
dc.date.issued2024
dc.departmentAlanya Alaaddin Keykubat Üniversitesi
dc.description.abstractDeep networks have been of considerable interest in literature and have enabled the solution of recent real-world applications. Due to filters that offer feature extraction, Convolutional Neural Network (CNN) is recognized as an accurate, efficient and trustworthy deep learning technique for the solution of image-based challenges. The high-performing CNNs are computationally demanding even if they produce good results in a variety of applications. This is because a large number of parameters limit their ability to be reused on central processing units with low performance. To address these limitations, we suggest a novel statistical filter-based CNN (HistStatCNN) for image classification. The convolution kernels of the designed CNN model were initialized by continuous statistical methods. The performance of the proposed filter initialization approach was evaluated on a novel histological dataset and various histopathological benchmark datasets. To prove the efficiency of statistical filters, three unique parameter sets and a mixed parameter set of statistical filters were applied to the designed CNN model for the classification task. According to the results, the accuracy of GoogleNet, ResNet18, ResNet50 and ResNet101 models were 85.56%, 85.24%, 83.59% and 83.79%, respectively. The accuracy was improved by 87.13% by HistStatCNN for the histological data classification task. Moreover, the performance of the proposed filter generation approach was proved by testing on various histopathological benchmark datasets, increasing average accuracy rates. Experimental results validate that the proposed statistical filters enhance the performance of the network with more simple CNN models.
dc.identifier.doi10.1111/ahe.13073
dc.identifier.issn0340-2096
dc.identifier.issn1439-0264
dc.identifier.issue4
dc.identifier.pmid38868912
dc.identifier.scopus2-s2.0-85196122824
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1111/ahe.13073
dc.identifier.urihttps://hdl.handle.net/20.500.12868/5079
dc.identifier.volume53
dc.identifier.wosWOS:001244683800001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherWiley
dc.relation.ispartofAnatomia Histologia Embryologia
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WoS_20260121
dc.subjectartificial intelligence
dc.subjectCNN
dc.subjectdeep learning
dc.subjectfeature extraction
dc.subjectimage classification
dc.subjectstatistical filter
dc.titleHistological tissue classification with a novel statistical filter-based convolutional neural network
dc.typeArticle

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