Deep transfer learning methods for classification colorectal cancer based on histology images - DURU, ADİL DENİZ (My University, 2022)
© 2022 IEEE.Deep transfer learning is one of the common techniques used to classify different types of cancer. The goal of this research is to focus on and adopt a fast, accurate, suitable, and reliable for classification of colorectal cancer. Digital histology images are adjustable to the application of convolutional neural networks (CNNs) for analysis and classification, due to the sheer size of pixel data present in them. Which can provide a lot of information about colorectal cancer. We used ten different types of pr-trained models with two type method of classification techniques namely (normal classification and k-fold crosse validation) to classify the tumor tissue, we used two different kinds of datasets were these datasets consisting of three classes (normal, low tumors, and high tumors). Among all these eight models of deep transfer learning, the highest accuracy achieved was 96.6% with Darknet53 for 5-Fold and for normal classification the highest results obtained was 98.7% for ResNet50. Moreover, we compared our result with many other papers in stat-of-the-art, the results obtained show clearly the proposed method was outperformed the other papers.
Keywords: Sosyal ve Beşeri Bilimler; Sosyoloji; Kütüphanecilik; Tarımsal Bilimler; Ziraat; Tarım Makineleri; Tarımda Enerji; Biyoyakıt Teknolojisi; Bilgi Sistemleri, Haberleşme ve Kontrol Mühendisliği; Kontrol ve Sistem Mühendisliği; Bilgisayar Bilimleri; Algoritmalar; İnşaat Mühendisliği; Mühendislik ve Teknoloji; Social Sciences and Humanities; Sociology; Library Sciences; Agricultural Sciences; Agriculture; Farm Machinery; Energy in Agriculture; Biofuels Technology; Information Systems, Communication and Control Engineering; Control and System Engineering; Computer Sciences; algorithms; Civil Engineering; Engineering and Technology; Mühendislik, Bilişim ve Teknoloji (ENG); Sosyal Bilimler (SOC); Bilgisayar Bilimi; Mühendislik; Sosyal Bilimler Genel; OTOMASYON & KONTROL SİSTEMLERİ; BİLGİSAYAR BİLİMİ, YAPAY ZEKA; TELEKOMÜNİKASYON; ENERJİ VE YAKITLAR; BİLGİ BİLİMİ VE KÜTÜPHANE BİLİMİ; MÜHENDİSLİK, İNŞAAT; Engineering, Computing & Technology (ENG); Social Sciences (SOC); COMPUTER SCIENCE; ENGINEERING; SOCIAL SCIENCES, GENERAL; AUTOMATION & CONTROL SYSTEMS; COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE; TELECOMMUNICATIONS; ENERGY & FUELS; INFORMATION SCIENCE & LIBRARY SCIENCE; ENGINEERING, CIVIL; Yapay Zeka; Fizik Bilimleri; Bilgisayar Ağları ve İletişim; Bilgisayar Bilimi Uygulamaları; Bilgi Sistemleri ve Yönetimi; Sosyal Bilimler ve Beşeri Bilimler; Yenilenebilir Enerji, Sürdürülebilirlik ve Çevre; İnşaat ve Yapı Mühendisliği; Kontrol ve Optimizasyon; Artificial Intelligence; Physical Sciences; Computer Networks and Communications; Computer Science Applications; Information Systems and Management; Social Sciences & Humanities; Renewable Energy, Sustainability and the Environment; Civil and Structural Engineering; Control and Optimization; Classification; CNN; Colorectal Cancer; Deep Learning; Transfer learning