Explainable Contrastive and Cost-Sensitive Learning for Cervical Cancer
  Classification
Ashfiqun Mustari Rushmia Ahmed Afsara Tasnim Jakia Sultana Juthi G M Shahariar
This paper proposes an efficient system for classifying cervical cancer cells
using pre-trained convolutional neural networks (CNNs). We first fine-tune five
pre-trained CNNs and minimize the overall cost of misclassification by
prioritizing accuracy for certain classes that have higher associated costs or
importance. To further enhance the performance of the models, supervised
contrastive learning is included to make the models more adept at capturing
important features and patterns. Extensive experimentation are conducted to
evaluate the proposed system on the SIPaKMeD dataset. The experimental results
demonstrate the effectiveness of the developed system, achieving an accuracy of
97.29%. To make our system more trustworthy, we have employed several
explainable AI techniques to interpret how the models reached a specific
decision. The implementation of the system can be found at -
https://github.com/isha-67/CervicalCancerStudy.