Scopus
YÖKSİS Eşleşti
Histological tissue classification with a novel statistical filter-based convolutional neural network
Journal of Veterinary Medicine Series C: Anatomia Histologia Embryologia · Temmuz 2024
YÖKSİS Kayıtları
Histological tissue classification with a novel statistical filter‐based convolutional neural network
Anatomia, Histologia, Embryologia · 2024 SCI-Expanded
PROFESÖR ŞAKİR TAŞDEMİR →
Histological tissue classification with a novel statistical filter‐based convolutional neural network
Anatomia, Histologia, Embryologia · 2024 SCI-Expanded
DOKTOR ÖĞRETİM ÜYESİ NEJAT ÜNLÜKAL →
Makale Bilgileri
DergiJournal of Veterinary Medicine Series C: Anatomia Histologia Embryologia
Yayın TarihiTemmuz 2024
Cilt / Sayfa53
Scopus ID2-s2.0-85196122824
Özet
Deep 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.
Yazarlar (5)
1
Nejat Ünlükal
ORCID: 0000-0002-8107-4882
2
Erkan Ülker
3
Merve Solmaz
4
Kübra Uyar
5
Şakir Taşdemir
Anahtar Kelimeler
artificial intelligence
CNN
deep learning
feature extraction
image classification
statistical filter
Kurumlar
Alanya Alaaddin Keykubat University
Alanya Turkey
Konya Technical University
Konya Turkey
Selçuk Üniversitesi
Selçuklu Turkey