Auxiliary classification of cervical cells based on multi-domain hybrid deep learning framework

• Multi-domain hybrid deep learning framework (MDHDN) is proposed for the classification of cervical cells. • Cell deep features from multi-domain (time and frequency) are extracted by the pretrained VGG-19 (Visual Geometry Group-19) with a hashing layer after the last fully connected layer. • Final cell diagnosis is generated by the correlation analysis on outputs of three subchannels. • The proposed approach obtains the similar performance with the state-of-the-art models using the novel structure, whose accuracy, sensitivity, and specificity are 98.7%, 98.2%, 98.9%. Computer-aided cervical cell classification using Pap smears or Thinprep cytologic test (TCT) have been widely applied as a high effective screening tool, by which the cells are classified into different subclasses. However, existing classification approaches mainly rely on single detection structure, like deep learning or hand-crafted methods, which have huge computation complexity and lower accuracy. So far, no cell spectrum is applied for classification. This paper addresses the limitations by making the first attempt to use the multi-domain hybrid deep learning framework (MDHDN) for the classification of cervical cells. Cell deep features from multi-domain (time and frequency) are extracted by the pretrained VGG-19 (Visual Geometry Group-19), which is the deep Convolutional Neural Network (CNN) with a hashing layer after the last fully connected layer. Hand-crafted features for the original images are processed with the feature selection, clustering and dimensionality reduction. Then the three subchannels of the proposed framework output the category results using the SVM classifier, the final cell diagnosis is generated by the correlation analysis. Results show that the proposed approach obtains the similar performance with the state-of-the-art models using the novel structure, whose accuracy, sensitivity, and specificity are 98.7%, 98.2%, 98.9% in Herlev dataset when applying five-fold cross-validation, respectively. Similar superior classification performance is achieved on the BJTU dataset, validation on the SIPaKMeD dataset also proves its generalization ability. The proposed novel screening framework is promising for the early diagnosis of cervical cancer, multi-domain and hybrid features are proved feasible in clinical practice.