Background: Early detection of cervical cancer may give life to women all over the
world. Pap-smear test and Human papillomavirus test are techniques used for the detection and prevention
of cervical cancer.
Objective: In this paper, pap-smear images are analysed and cells are classified using stacked autoencoder
based deep neural network. Pap-smear cells are classified into 2 classes and 4 classes. Twoclass
classification includes classification of cells in normal and abnormal cells while four-class
classification includes classification of cells in normal cells , mild dysplastic cells, moderate dysplastic
cells and severe dysplastic cells.
Methods: The features are extracted by deep neural networks based on their architecture. Proposed
deep neural networks consist of three stacked auto encoders with hidden sizes 512, 256 and 128, respectively.
Softmax used as the outer layer for the classification of pap smear cells.
Results: Average accuracy achieved for 2-class classification among normal and abnormal cells is
98.2 % while for 4-class classification among normal, mild, moderate and severe dysplastic cells is
93.8 % respectively.
Conclusion: The proposed approach avoids image segmentation and feature extraction applied by
previous works. This study highlights deep learning as an important tool for cells classification of
pap-smear images. The accuracy of the proposed method may vary with the different combination of
hidden size and number of autoencoders.