我所郏东耀老师课题组完成的论文《Cervical cell classification based on strong feature CNN-LSVM network using Adaboost optimization》于2023-01在《Journal of Intelligent & Fuzzy Systems》正式发表。论文摘要如下：
Abstract: Cervical cancer is one of the most common causes of death in women in the world, and early screening is an effective means of diagnosis and treatment, which can greatly improve the survival rate. Cervical cell classification model is an effective means to assist screening. However, the existing single model, including CNNs and machine learning methods, still has shortcomings such as unclear feature meaning, low accuracy and insufficient supervision. To solve the shortcomings of a single model, a novel framework based on strong feature Convolutional Neural Networks (CNN)-Lagrangian Support Vector Machine (LSVM) model is proposed for the accurate classification of cervical cells. Strong features extracted by hybrid methods are fused with the abstract ones from hidden layers of LeNet-5, then the fused features are processed with dimension reduction and fed into the LSVM classifier optimized by Adaboost for classification. Proposed model is evaluated using the augmented Herlev and private dataset with the metrics including accuracy (Acc), sensitivity (Sn), and specificity (Sp), which outperformed the baselines and state-of-the-art approaches with the Acc of 99.5% and 94.2% in 2&7-class classification, respectively.
引用信息：Nengkai Wu, Dongyao Jia, Chuanwang Zhang, Ziqi Li. Cervical cell classification based on strong feature CNN-LSVM network using Adaboost optimization. Journal of Intelligent & Fuzzy Systems, 2023-01. DOI: 10.3233/jifs-221604