Histological tissue classification with a novel statistical filter‐based convolutional neural network
Yazarlar (5)
Dr. Öğr. Üyesi Nejat Ünlükal Selçuk Üniversitesi, Türkiye
Prof. Dr. Erkan Ülker Konya Technical University, Türkiye
Öğr. Gör. Merve Solmaz Selçuk Üniversitesi, Türkiye
Dr. Öğr. Üyesi Kübra Uyar Alanya Alaaddin Keykubat University, Türkiye
Prof. Dr. Şakir TAŞDEMİR Sinop Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | Journal of Veterinary Medicine Series C Anatomia Histologia Embryologia (Q3) | ||
| Dergi ISSN | 0340-2096 Dergi Bilgileri (2024) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | Türkçe | Basım Tarihi | 05-2024 |
| Cilt / Sayı / Sayfa | 53 / 4 / – | DOI | 10.1111/ahe.13073 |
| Makale Linki | https://doi.org/10.1111/ahe.13073 | ||
| UAK Araştırma Alanları |
Yapay Zeka
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| 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 … |
| Anahtar Kelimeler |
| artificial intelligence | CNN | deep learning | feature extraction | image classification | statistical filter |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 2 |
| Scopus | 1 |
| Google Scholar | 3 |