Detection of cervical cells based on improved SSD network

Cervical cancer has influenced life of women worldwide as the fourth most severe cancer. Early screening, detection and treatment of cervical cancer notably increase the life quality and reduce the death rate of patients. Therefore, automatic diagnosis of cervical cancer could bridge the gap between testing needs and capabilities. Cervical cell detection plays an important role in cancer screening, the intent of this study is to classify the cervical cells through deep learning models, which helps to monitor the patients’ health. SSD (Single Shot MultiBox Detector) network is integrated with the positive and negative features to address the shortcomings of insufficient sensitivity to small objects. Besides, center loss function is added to better address situations that intra-class differences are greater than inter-class differences. A dataset containing 1462 benchmarked cervical cells was utilized. 80% (1167) are used for training and the remaining 20% (295) are allocated for testing. Proposed optimized SSD network achieved the accuracy of 90.8% and mAP (mean Average Precision) of 81.53%, which is 7.54% and 4.92% higher than YOLO (You Only Look Once) and classical SSD, respectively. The addition of complementary features improves the network sensitivity and the overall accuracy. It is also concluded that the proposed SSD network could be applied in cell classification for the early automatic detection of cervical cancer.