Deep Learning Enabled Segmentation, Classification and Risk Assessment of Cervical Cancer
This work addresses early detection of cervical cancer, a major health issue for women, by improving automated analysis of Pap smear images, though it appears incremental with performance similar to state-of-the-art models.
The study tackled cervical cancer detection by proposing a novel deep learning architecture for segmenting and classifying cells from Pap smear images, achieving an Intersection over Union score of 0.83 and 90% classification accuracy with 1.7 million parameters, and introduced a risk assessment method for predicting malignancy progression.
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.