Deep Learning Enabled Segmentation, Classification and Risk Assessment of Cervical Cancer

TL;DR

This paper presents a novel deep learning framework for cervical cancer detection that combines segmentation, classification, and risk assessment, achieving high accuracy with fewer parameters and enabling early diagnosis and prognosis.

Contribution

A new Multi-Resolution Fusion Deep Convolutional Network architecture and a multi-task learning approach for improved cervical cancer image analysis.

Findings

Achieved 90% classification accuracy.

Intersection over Union score of 0.83 for segmentation.

Model uses 85 times fewer parameters than VGG-19.

Abstract

Cervical cancer, the fourth leading cause of cancer in women globally, requires early detection through Pap smear tests to identify precancerous changes and prevent disease progression. In this study, we performed a focused analysis by segmenting the cellular boundaries and drawing bounding boxes to isolate the cancer cells. A novel Deep Learning (DL) architecture, the ``Multi-Resolution Fusion Deep Convolutional Network", was proposed to effectively handle images with varying resolutions and aspect ratios, with its efficacy showcased using the SIPaKMeD dataset. The performance of this DL model was observed to be similar to the state-of-the-art models, with accuracy variations of a mere 2\% to 3\%, achieved using just 1.7 million learnable parameters, which is approximately 85 times less than the VGG-19 model. Furthermore, we introduced a multi-task learning technique that simultaneously performs segmentation and classification tasks and begets an Intersection over Union score of 0.83 and a classification accuracy of 90\%. The final stage of the workflow employs a probabilistic approach for risk assessment, extracting feature vectors to predict the likelihood of normal cells progressing to malignant states, which can be utilized for the prognosis of cervical cancer.