Classification of Pulmonary CT Images by Using Hybrid 3D-Deep Convolutional Neural Network Architecture

被引:71
|
作者
Polat, Huseyin [1 ]
Mehr, Homay Danaei [1 ]
机构
[1] Gazi Univ, Dept Comp Engn, Fac Technol, TR-06500 Ankara, Turkey
来源
APPLIED SCIENCES-BASEL | 2019年 / 9卷 / 05期
关键词
computed tomography; convolutional neural network; deep learning; lung cancer diagnosis; medical imaging; SVM classifier; LUNG-CANCER;
D O I
10.3390/app9050940
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Lung cancer is the most common cause of cancer-related deaths worldwide. Hence, the survival rate of patients can be increased by early diagnosis. Recently, machine learning methods on Computed Tomography (CT) images have been used in the diagnosis of lung cancer to accelerate the diagnosis process and assist physicians. However, in conventional machine learning techniques, using handcrafted feature extraction methods on CT images are complicated processes. Hence, deep learning as an effective area of machine learning methods by using automatic feature extraction methods could minimize the process of feature extraction. In this study, two Convolutional Neural Network (CNN)-based models were proposed as deep learning methods to diagnose lung cancer on lung CT images. To investigate the performance of the two proposed models (Straight 3D-CNN with conventional softmax and hybrid 3D-CNN with Radial Basis Function (RBF)-based SVM), the altered models of two-well known CNN architectures (3D-AlexNet and 3D-GoogleNet) were considered. Experimental results showed that the performance of the two proposed models surpassed 3D-AlexNet and 3D-GoogleNet. Furthermore, the proposed hybrid 3D-CNN with SVM achieved more satisfying results (91.81%, 88.53% and 91.91% for accuracy rate, sensitivity and precision respectively) compared to straight 3D-CNN with softmax in the diagnosis of lung cancer.
引用
收藏
页数:15
相关论文
共 50 条
  • [21] Deep Learning in CT Images: Automated Pulmonary Nodule Detection for Subsequent Management Using Convolutional Neural Network
    Xu, Yi-Ming
    Zhang, Teng
    Xu, Hai
    Qi, Liang
    Zhang, Wei
    Zhang, Yu-Dong
    Gao, Da-Shan
    Yuan, Mei
    Yu, Tong-Fu
    [J]. CANCER MANAGEMENT AND RESEARCH, 2020, 12 : 2979 - 2992
  • [22] Unsupervised Land Cover Classification of Hybrid and Dual-Polarized Images Using Deep Convolutional Neural Network
    Chatterjee, Ankita
    Saha, Jayasree
    Mukherjee, Jayanta
    Aikat, Subhas
    Misra, Arundhati
    [J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2021, 18 (06) : 969 - 973
  • [23] Deep Convolutional Neural Network based Ship Images Classification
    Mishra, Narendra Kumar
    Kumar, Ashok
    Choudhury, Kishor
    [J]. DEFENCE SCIENCE JOURNAL, 2021, 71 (02) : 200 - 208
  • [24] Classification of Deep Convolutional Neural Network in Thyroid Ultrasound Images
    Hui, Ran
    Chen, Jiaxing
    Liu, Yu
    Shi, Lin
    Fu, Chao
    Ishsay, Ostfeld
    [J]. JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS, 2020, 10 (08) : 1943 - 1948
  • [25] Developing a Hybrid Network Architecture for Deep Convolutional Neural Networks
    Sayan, H. Huseyin
    Tekgozoglu, O. Faruk
    Sonmez, Yusuf
    Turan, Bilal
    [J]. ARTIFICIAL INTELLIGENCE AND APPLIED MATHEMATICS IN ENGINEERING PROBLEMS, 2020, 43 : 750 - 757
  • [26] Automated pulmonary nodule detection in CT images using deep convolutional neural networks
    Xie, Hongtao
    Yang, Dongbao
    Sun, Nannan
    Chen, Zhineng
    Zhang, Yongdong
    [J]. PATTERN RECOGNITION, 2019, 85 : 109 - 119
  • [27] Detection of tumors on brain MRI images using the hybrid convolutional neural network architecture
    Cinar, Ahmet
    Yildirim, Muhammed
    [J]. MEDICAL HYPOTHESES, 2020, 139
  • [28] Classification of Histopathological Images Using Convolutional Neural Network
    Hatipoglu, Nuh
    Bilgin, Gokhan
    [J]. 2014 4TH INTERNATIONAL CONFERENCE ON IMAGE PROCESSING THEORY, TOOLS AND APPLICATIONS (IPTA), 2014, : 295 - 300
  • [29] Classification of Tank Images Using Convolutional Neural Network
    Liu, Ying
    Yu, Yongbin
    Wang, Lin
    Nyima, Tashi
    Zhaxi, Nima
    Huang, Hang
    Deng, Quanxin
    [J]. PROCEEDINGS OF 2020 IEEE 4TH INFORMATION TECHNOLOGY, NETWORKING, ELECTRONIC AND AUTOMATION CONTROL CONFERENCE (ITNEC 2020), 2020, : 210 - 214
  • [30] A Survey on Image Classification and Activity Recognition using Deep Convolutional Neural Network Architecture
    Sornam, M.
    Muthusubash, Kavitha
    Vanitha, V.
    [J]. 2017 NINTH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTING (ICOAC), 2017, : 121 - 126