Cervical cancer detection in pap smear whole slide images using convNet with transfer learning and progressive resizing

Cervical intraepithelial neoplasia (CIN) and cervical cancer are major health problems faced by women worldwide. The conventional Papanicolaou (Pap) smear analysis is an effective method to diagnose cervical pre-malignant and malignant conditions by analyzing swab images. Various computer vision tec...

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Bibliographic Details
Published in:	PeerJ. Computer science Vol. 7; p. e348
Main Authors:	Bhatt, Anant R, Ganatra, Amit, Kotecha, Ketan
Format:	Journal Article
Language:	English
Published:	United States PeerJ. Ltd 18-02-2021 PeerJ, Inc PeerJ Inc
Subjects:	Analysis Artificial Intelligence Artificial neural networks Cable television broadcasting industry Cancer Cervical cancer Cervical cytology Classification Computer Vision Convolution Convolution neural network Cytoplasm Data Mining and Machine Learning Data Science Datasets Deep learning Developing countries Experiments Feature extraction Health aspects Human papillomavirus Image classification LDCs Learning Lesions Localization Machine vision Medical imaging Medical screening Morphology Neural networks Pap smear Pap test Papanicolaou smear Progressive resizing Superposition (mathematics) Support vector machines Training Transfer learning Women Womens health United States Cervical cytology Deep learning Progressive resizing Convolution neural network Transfer learning Papanicolaou smear Herlev Cervical cancer Metamorphic analysis Sipakmed
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Summary:	Cervical intraepithelial neoplasia (CIN) and cervical cancer are major health problems faced by women worldwide. The conventional Papanicolaou (Pap) smear analysis is an effective method to diagnose cervical pre-malignant and malignant conditions by analyzing swab images. Various computer vision techniques can be explored to identify potential precancerous and cancerous lesions by analyzing the Pap smear image. The majority of existing work cover binary classification approaches using various classifiers and Convolution Neural Networks. However, they suffer from inherent challenges for minute feature extraction and precise classification. We propose a novel methodology to carry out the multiclass classification of cervical cells from Whole Slide Images (WSI) with optimum feature extraction. The actualization of Conv Net with Transfer Learning technique substantiates meaningful Metamorphic Diagnosis of neoplastic and pre-neoplastic lesions. As the Progressive Resizing technique (an advanced method for training ConvNet) incorporates prior knowledge of the feature hierarchy and can reuse old computations while learning new ones, the model can carry forward the extracted morphological cell features to subsequent Neural Network layers iteratively for elusive learning. The Progressive Resizing technique superimposition in consultation with the Transfer Learning technique while training the Conv Net models has shown a substantial performance increase. The proposed binary and multiclass classification methodology succored in achieving benchmark scores on the Herlev Dataset. We achieved singular multiclass classification scores for WSI images of the SIPaKMed dataset, that is, accuracy (99.70%), precision (99.70%), recall (99.72%), F-Beta (99.63%), and Kappa scores (99.31%), which supersede the scores obtained through principal methodologies. GradCam based feature interpretation extends enhanced assimilation of the generated results, highlighting the pre-malignant and malignant lesions by visual localization in the images.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	2376-5992 2376-5992
DOI:	10.7717/peerj-cs.348