Internet of health things-driven deep learning system for detection and classification of cervical cells using transfer learning,The Journal of Supercomputing

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Internet of health things-driven deep learning system for detection and classification of cervical cells using transfer learning
The Journal of Supercomputing ( IF 3.3 ) Pub Date : 2020-01-14 , DOI: 10.1007/s11227-020-03159-4
Aditya Khamparia , Deepak Gupta , Victor Hugo C. de Albuquerque , Arun Kumar Sangaiah , Rutvij H. Jhaveri

Cervical cancer is one of the fastest growing global health problems and leading cause of mortality among women of developing countries. Automated Pap smear cell recognition and classification in early stage of cell development is crucial for effective disease diagnosis and immediate treatment. Thus, in this article, we proposed a novel internet of health things (IoHT)-driven deep learning framework for detection and classification of cervical cancer in Pap smear images using concept of transfer learning. Following transfer learning, convolutional neural network (CNN) was combined with different conventional machine learning techniques like K nearest neighbor, naïve Bayes, logistic regression, random forest and support vector machines. In the proposed framework, feature extraction from cervical images is performed using pre-trained CNN models like InceptionV3, VGG19, SqueezeNet and ResNet50, which are fed into dense and flattened layer for normal and abnormal cervical cells classification. The performance of the proposed IoHT frameworks is evaluated using standard Pap smear Herlev dataset. The proposed approach was validated by analyzing precision, recall, F1-score, training–testing time and support parameters. The obtained results concluded that CNN pre-trained model ResNet50 achieved the higher classification rate of 97.89% with the involvement of random forest classifier for effective and reliable disease detection and classification. The minimum training time and testing time required to train model were 0.032 s and 0.006 s, respectively.

更新日期：2020-01-14

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