Abstract
Cervical cells classification is a crucial component of computer-aided cervical cancer detection. Fine-grained classification is of great clinical importance when guiding clinical decisions on the diagnoses and treatment, which remains very challenging. Recently, convolutional neural networks (CNN) provide a novel way to classify cervical cells by using automatically learned features. Although the ensemble of CNN models can increase model diversity and potentially boost the classification accuracy, it is a multi-step process, as several CNN models need to be trained respectively and then be selected for ensemble. On the other hand, due to the small training samples, the advantages of powerful CNN models may not be effectively leveraged. In order to address such a challenging issue, this paper proposes a transfer learning based snapshot ensemble (TLSE) method by integrating snapshot ensemble learning with transfer learning in a unified and coordinated way. Snapshot ensemble provides ensemble benefits within a single model training procedure, while transfer learning focuses on the small sample problem in cervical cells classification. Furthermore, a new training strategy is proposed for guaranteeing the combination. The TLSE method is evaluated on a pap-smear dataset called Herlev dataset and is proved to have some superiorities over the exiting methods. It demonstrates that TLSE can improve the accuracy in an ensemble manner with only one single training process for the small sample in fine-grained cervical cells classification.
Highlights
Cervical cancer continues to be one of the prevalent cancers affecting women worldwide [1].The disease is the most common cancer among women in 39 countries, and is the leading cause of cancer dearth in women in 45 countries [2]
The Classification Results of transfer learning based snapshot ensemble (TLSE) Method and Transfer Learning Method Based On Different convolutional neural networks (CNN) Models
Three different CNN architectures are chosen to be base models: VGG model [50], ResNet-18 [51], and Inception-ResNet [52] model. The architectures of these three pre-trained CNN models are shown in Figures 5–7, respectively. The aim of this experiment was to show the adaptability of the TLSE, whether it can be adopted in the training of different CNN models
Summary
Cervical cancer continues to be one of the prevalent cancers affecting women worldwide [1].The disease is the most common cancer among women in 39 countries, and is the leading cause of cancer dearth in women in 45 countries [2]. Cervical cancer continues to be one of the prevalent cancers affecting women worldwide [1]. The disease affects predominantly women in lower-resource countries, almost 70% of the global burden occurs in areas with low or medium levels of human development [2]. Screening abnormal cells from a pap-smear image has been a widely accepted method for prevention management as well as early detection of cervical cancer, especially in the developing countries [3]. The complex nature of cervical cell images presents significant challenges for manual screen analysis, the diagnosis results heavily rely on the experience of the technicians. The automation of cervical cells classification is essential for the development of a computer-aided classification system with low cost, adequate speed, and high accuracy [4], so that researchers and doctors can be released from the boring and repeated routine work. The computer-aided system can reduce the bias and provide robust results
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
