.png){kind=icon}
返回
A deep ensemble learning method for colorectal polyp classification with optimized network parameters
.png){kind=icon}
OA
AI
DOI：10.1007/s10489-022-03689-9.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Colorectal Cancer (CRC), a leading cause of cancer-related deaths, can be abated by timely polypectomy. Computer-aided classification of polyps helps endoscopists to resect timely without submitting the sample for histology. Deep learning-based algorithms are promoted for computer-aided colorectal polyp classification. However, the existing methods do not accommodate any information on hyperparametric settings essential for model optimisation. Furthermore, unlike the polyp types, i.e., hyperplastic and adenomatous, the third type, serrated adenoma, is difficult to classify due to its hybrid nature. Moreover, automated assessment of polyps is a challenging task due to the similarities in their patterns; therefore, the strength of individual weak learners is combined to form a weighted ensemble model for an accurate classification model by establishing the optimised hyperparameters. In contrast to existing studies on binary classification, multiclass classification require evaluation through advanced measures. This study compared six existing Convolutional Neural Networks in addition to transfer learning and opted for optimum performing architecture only for ensemble models. The performance evaluation on UCI and PICCOLO dataset of the proposed method in terms of accuracy (96.3%, 81.2%), precision (95.5%, 82.4%), recall (97.2%, 81.1%), F1-score (96.3%, 81.3%) and model reliability using Cohen's Kappa Coefficient (0.94, 0.62) shows the superiority over existing models. The outcomes of experiments by other studies on the same dataset yielded 82.5% accuracy with 72.7% recall by SVM and 85.9% accuracy with 87.6% recall by other deep learning methods. The proposed method demonstrates that a weighted ensemble of optimised networks along with data augmentation significantly boosts the performance of deep learning-based CAD.
Keyword:
Colorectal Cancer
Deep learning
Ensemble learning
Prediction
Transfer learning
Virtual biopsy
期刊
IF:
3.5
论文数:
7.6K
被引数:
1.7W
机构
引用论文
Artificial Intelligence-Assisted Polyp Detection for Colonoscopy: Initial Experience.png)
GASTROENTEROLOGY
OAAI
GASTROENTEROLOGYIF25.1
OAAI分享收藏分享收藏Plasma Hsp90 levels in patients with systemic sclerosis and relation to lung and skin involvement: a cross-sectional and longitudinal study系统性硬化症患者的血浆Hsp90水平及其与肺和皮肤受累的关系: 一项横断面和纵向研究SCIENTIFIC REPORTSOAAI
SCIENTIFIC REPORTSIF3.9
OAAI分享收藏Colorectal Disease Classification Using Efficiently Scaled Dilation in Convolutional Neural Network在卷积神经网络中使用有效的缩放扩张进行结直肠疾病分类IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏Computer-Aided Classification of Gastrointestinal Lesions in Regular Colonoscopy常规结肠镜检查中胃肠道病变的计算机辅助分类OAAI
OAAI分享收藏Artificial intelligence and colonoscopy: Current status and future perspectivesDIGESTIVE ENDOSCOPYOAAI
DIGESTIVE ENDOSCOPYIF4.7
OAAI分享收藏
查看更多内容