HIC-net: A deep convolutional neural network model for classification of histopathological breast images
Yükleniyor...
Dosyalar
Tarih
2019
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
PERGAMON-ELSEVIER SCIENCE LTD
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
In this study, a convolutional neural network (CNN) model is presented to automatically identify cancerous areas on whole-slide histopathological images (WSI). The proposed WSI classification network (HIC-net) architecture performs window-based classification by dividing the WSI into a certain plane. In our method, an effective pre-processing step has been added for WSI for better predictability of image parts and faster training. A large dataset containing 30,656 images is used for the evaluation of the HIC-net algorithm. Of these images, 23,040 are used for training, 2560 are used for validation and 5056 are used for testing. HIC-net has more successful results than other state-of-art CNN algorithms with AUC score of 97.7%. If we evaluate the classification results of HIC-net using softmax function, HIC-net success rates have 96.71% sensitivity, 95.7% specificity, 96.21% accuracy, and are more successful than other state-of-the-art techniques which are used in cancer research. (C) 2019 Elsevier Ltd. All rights reserved.
Açıklama
Anahtar Kelimeler
Cancer classification, Convolutional neural networks, CNN, Histopathological image, Whole-slide
Kaynak
COMPUTERS & ELECTRICAL ENGINEERING
WoS Q Değeri
Q2
Scopus Q Değeri
Q1
Cilt
76
Sayı
Künye
Öztürk, Ş., Akdemir, B. (2019). HIC-net: A Deep Convolutional Neural Network Model for Classification of Histopathological Breast Images. Computers and Electrical Engineering, 76, 299-310.
