Automatic classification of solitary pulmonary nodules in PET/CT imaging employing transfer learning techniques

被引:27
作者
Apostolopoulos, Ioannis D. [1 ]
Pintelas, Emmanuel G. [2 ]
Livieris, Ioannis E. [2 ]
Apostolopoulos, Dimitris J. [3 ]
Papathanasiou, Nikolaos D. [3 ]
Pintelas, Panagiotis E. [2 ]
Panayiotakis, George S. [1 ]
机构
[1] Univ Patras, Sch Med, Dept Med Phys, Patras 26504, Greece
[2] Univ Patras, Dept Math, Patras 26504, Greece
[3] Univ Patras, Lab Nucl Med, Patras 26504, Greece
关键词
Solitary pulmonary nodule classification; Deep learning; Convolutional Neural Networks; Transfer learning; Data augmentation; CANCER; MODEL;
D O I
10.1007/s11517-021-02378-y
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Early and automatic diagnosis of Solitary Pulmonary Nodules (SPN) in Computed Tomography (CT) chest scans can provide early treatment for patients with lung cancer, as well as doctor liberation from time-consuming procedures. The purpose of this study is the automatic and reliable characterization of SPNs in CT scans extracted from Positron Emission Tomography and Computer Tomography (PET/CT) system. To achieve the aforementioned task, Deep Learning with Convolutional Neural Networks (CNN) is applied. The strategy of training specific CNN architectures from scratch and the strategy of transfer learning, by utilizing state-of-the-art pre-trained CNNs, are compared and evaluated. To enhance the training sets, data augmentation is performed. The publicly available database of CT scans, named as Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), is also utilized to further expand the training set and is added to the PET/CT dataset. The results highlight the effectiveness of transfer learning and data augmentation for the classification task of small datasets. The best accuracy obtained on the PET/CT dataset reached 94%, utilizing a modification proposal of a state-of-the-art CNN, called VGG16, and enhancing the training set with LIDC-IDRI dataset. Besides, the proposed modification outperforms in terms of sensitivity several similar researches, which exploit the benefits of transfer learning.
引用
收藏
页码:1299 / 1310
页数:12
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