CMT-CNN: colposcopic multimodal temporal hybrid deep learning model to detect cervical intraepithelial neoplasia

Abstract

Cervical cancer poses a significant threat to women's health in developing countries, necessitating effective early detection methods. In this study, we introduce the Colposcopic Multimodal Temporal Convolution Neural Network (CMT-CNN), a novel model designed for classifying cervical intraepithelial neoplasia by leveraging sequential colposcope images and integrating extracted features with clinical data. Our approach incorporates Mask R-CNN for precise cervix region segmentation and deploys the EfficientNet B7 architecture to extract features from saline, iodine, and acetic acid images. The fusion of clinical data at the decision level, coupled with Atrous Spatial Pyramid Pooling-based classification, yields remarkable results: an accuracy of 92.31%, precision of 90.19%, recall of 89.63%, and an F-1 score of 90.72. This achievement not only establishes the superiority of the CMT-CNN model over baselines but also paves the way for future research endeavours aiming to harness heterogeneous data types in the development of deep learning models for cervical cancer screening. The implications of this work are profound, offering a potent tool for early cervical cancer detection that combines multimodal data and clinical insights, potentially saving countless lives.

Keywords

• cervical cancer
• efficientnet
• attention mechanism
• deep learning
• specular reflections