Classification of Cervical Cytology Overlapping Cell Images with Transfer Learning Architectures

Abstract

Cervical cell classification is a clinical biomarker in cervical cancer screening at early stages. An accurate and early diagnosis plays a vital role in preventing the cervical cancer. Recently, transfer learning using deep convolutional neural networks; have been deployed in many biomedical applications. The proposed work aims at applying the cutting edge pre-trained networks: AlexNet, ImageNet and Places365, to cervix images to detect the cancer. These pre-trained networks are fine-tuned and retrained for cervical cancer augmented data with benchmark CERVIX93 dataset available publically. The models were evaluated on performance measures viz; accuracy, precision, sensitivity, specificity, F-Score, MCC and kappa score. The results reflect that the AlexNet model is best for cervical cancer prediction with 99.03% accuracy and 0.98 of kappa coefficient showing a perfect agreement. Finally, the significant success rate makes the AlexNet model a useful assistive tool for radiologist and clinicians to detect the cervical cancer from pap-smear cytology images.