Improving efficiency in convolutional neural networks with 3D image filters
Yazarlar (5)
Dr. Öğr. Üyesi Kübra Uyar Selçuk Üniversitesi, Türkiye
Öğr. Gör. Merve Solmaz Selçuk Üniversitesi, Türkiye
Prof. Dr. Şakir TAŞDEMİR Selçuk Üniversitesi, Türkiye
Dr. Öğr. Üyesi Nejat Ünlükal Selçuk Üniversitesi, Türkiye
Prof. Dr. Erkan Ülker Konya Technical University, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Biomedical Signal Processing and Control (Q1) | ||
| Dergi ISSN | 1746-8094 Dergi Bilgileri (2022) | ||
| Dergi Tarandığı Indeksler | SCI | ||
| Makale Dili | İngilizce | Basım Tarihi | 01-2022 |
| Cilt / Sayı / Sayfa | 74 / 1 / 103563–0 | DOI | 10.1016/j.bspc.2022.103563 |
| Makale Linki | https://www.sciencedirect.com/science/article/abs/pii/S1746809422000854 | ||
| UAK Araştırma Alanları |
Yapay Zeka
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| Özet |
| Background and objective The effective performance of deep networks has provided the solution to various state-of-the-art problems. Convolutional Neural Network (CNN) is accepted as an accurate, effective, and reliable practice in image-based applications. However, there is a need to use pre-trained models in case of insufficient data in CNN. This study aims to present an alternative solution to this problem with the proposed 3D image-based filter generation approach with simpler CNNs for the classification of small datasets. Methods In this study, a novel 3D image filters-based CNN (Hist3DCNN) is proposed. The proposed filter generation approach is based on 3D object images taken from different perspectives. The efficiency of Hist3DCNN is shown on a novel histological dataset that contains blood, connective, epithelium, muscle, and nerve tissue images. Various case studies are carried out with generated … |
| Anahtar Kelimeler |
| 3D filter | Classification | CNN | Filter generation | Histological image |
Science Direct
Improving efficiency in convolutional neural networks with 3D image filters
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 7 |
| Scopus | 8 |
| Google Scholar | 10 |