Combining Convolutional and Recurrent Neural Networks for Classification of Focal Liver Lesions in Multi-phase CT Images

2018 ◽  
pp. 666-675 ◽  
Author(s):  
Dong Liang ◽  
Lanfen Lin ◽  
Hongjie Hu ◽  
Qiaowei Zhang ◽  
Qingqing Chen ◽  
...  
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Ct Images ◽  
Focal Liver Lesions ◽  
Liver Lesions ◽  
Multi Phase

Tensor-based sparse representations of multi-phase medical images for classification of focal liver lesions

2020 ◽  
Vol 130 ◽  
pp. 207-215 ◽  
Author(s):  
Jian Wang ◽  
Jing Li ◽  
Xian-Hua Han ◽  
Lanfen Lin ◽  
Hongjie Hu ◽  
...  
Keyword(s):  
Medical Images ◽  
Sparse Representations ◽  
Focal Liver Lesions ◽  
Liver Lesions ◽  
Multi Phase

scholarly journals Automatic Detection and Classification of Focal Liver Lesions Based on Deep Convolutional Neural Networks: A Preliminary Study

2021 ◽  
Vol 10 ◽  
Author(s):  
Jiarong Zhou ◽  
Wenzhe Wang ◽  
Biwen Lei ◽  
Wenhao Ge ◽  
Yu Huang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Automatic Detection ◽  
Classification Performance ◽  
Focal Liver Lesions ◽  
Liver Lesions ◽  
Deep Convolutional Neural Networks ◽  
Average Accuracy ◽  
Preliminary Study

With the increasing daily workload of physicians, computer-aided diagnosis (CAD) systems based on deep learning play an increasingly important role in pattern recognition of diagnostic medical images. In this paper, we propose a framework based on hierarchical convolutional neural networks (CNNs) for automatic detection and classification of focal liver lesions (FLLs) in multi-phasic computed tomography (CT). A total of 616 nodules, composed of three types of malignant lesions (hepatocellular carcinoma, intrahepatic cholangiocarcinoma, and metastasis) and benign lesions (hemangioma, focal nodular hyperplasia, and cyst), were randomly divided into training and test sets at an approximate ratio of 3:1. To evaluate the performance of our model, other commonly adopted CNN models and two physicians were included for comparison. Our model achieved the best results to detect FLLs, with an average test precision of 82.8%, recall of 93.4%, and F1-score of 87.8%. Our model initially classified FLLs into malignant and benign and then classified them into more detailed classes. For the binary and six-class classification, our model achieved average accuracy results of 82.5 and73.4%, respectively, which were better than the other three classification neural networks. Interestingly, the classification performance of the model was placed between a junior physician and a senior physician. Overall, this preliminary study demonstrates that our proposed multi-modality and multi-scale CNN structure can locate and classify FLLs accurately in a limited dataset, and would help inexperienced physicians to reach a diagnosis in clinical practice.

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