Research Article
DeepCerviCancer - Deep Learning-Based Cervical Image Classification using Colposcopy and Cytology Images
@ARTICLE{10.4108/eetpht.9.3473, author={Madhura Kalbhor and Swati Shinde and Sagar Lahade and Tanupriya Choudhury}, title={DeepCerviCancer - Deep Learning-Based Cervical Image Classification using Colposcopy and Cytology Images}, journal={EAI Endorsed Transactions on Pervasive Health and Technology}, volume={9}, number={1}, publisher={EAI}, journal_a={PHAT}, year={2023}, month={10}, keywords={computer vision, smart Healthcare, Artificial Intelligence, Internet of Things, MoveNet, Pose estimation, Machine Learning, deep learning, KNN, SVM, LDA}, doi={10.4108/eetpht.9.3473} }- Madhura Kalbhor
Swati Shinde
Sagar Lahade
Tanupriya Choudhury
Year: 2023
DeepCerviCancer - Deep Learning-Based Cervical Image Classification using Colposcopy and Cytology Images
PHAT
EAI
DOI: 10.4108/eetpht.9.3473
Abstract
INTRODUCTION: Cervical cancer is a deadly malignancy in the cervix, affecting billions of women annually. OBJECTIVES: To develop deep learning-based system for effective cervical cancer detection by combining colposcopy and cytology screening. METHODS: It employs DeepColpo for colposcopy and DeepCyto+ for cytology images. The models are trained on multiple datasets, including the self-collected cervical cancer dataset named Malhari, IARC Visual Inspection with Acetic Acid (VIA) Image Bank, IARC Colposcopy Image Bank, and Liquid-based Cytology Pap smear dataset. The ensemble model combines DeepColpo and DeepCyto+, using machine learning algorithms. RESULTS: The ensemble model achieves perfect recall, accuracy, F1 score, and precision on colposcopy and cytology images from the same patients. CONCLUSION: By combining modalities for cervical cancer screening and conducting tests on colposcopy and cytology images from the same patients, the novel approach achieved flawless results.
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