Summary of Explainable Contrastive and Cost-sensitive Learning For Cervical Cancer Classification, by Ashfiqun Mustari et al.
Explainable Contrastive and Cost-Sensitive Learning for Cervical Cancer Classification
by Ashfiqun Mustari, Rushmia Ahmed, Afsara Tasnim, Jakia Sultana Juthi, G M Shahariar
First submitted to arxiv on: 24 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed system efficiently classifies cervical cancer cells using pre-trained convolutional neural networks (CNNs). Fine-tuning five CNNs minimizes the overall cost of misclassification by prioritizing accuracy for classes with higher costs or importance. Supervised contrastive learning enhances model performance by capturing important features and patterns. Experimental results on the SIPaKMeD dataset demonstrate an accuracy of 97.29%. Explainable AI techniques ensure system trustworthiness, while implementation details can be found at https://github.com/isha-67/CervicalCancerStudy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses special computers to help doctors better diagnose cervical cancer cells. It combines ideas from machine learning and computer vision to make a more accurate diagnosis tool. The researchers used five different types of neural networks, fine-tuned them for the specific task, and then tested how well they worked on real data. They also added extra steps to help explain why the computers made certain decisions. |
Keywords
* Artificial intelligence * Fine tuning * Machine learning * Supervised
