A study on Deep Convolutional Neural Networks, transfer learning, and Mnet model for Cervical Cancer Detection
2025-09-24
Summary
The study introduces a lightweight convolutional neural network (CNN) model called S-Net for cervical cancer detection using Pap smear images. S-Net, alongside six state-of-the-art (SOTA) CNN models, achieves high accuracy while being computationally efficient, making it suitable for real-time applications in resource-constrained settings. The study also employs Explainable AI (XAI) techniques like SHAP, LIME, and Grad-CAM to enhance model transparency and interpretability.
Why This Matters
Cervical cancer remains a significant health challenge, particularly in low-resource settings where traditional diagnostic methods can be limited by high false-positive rates and human error. The development of computationally efficient and highly accurate models like S-Net can improve early detection and diagnosis, potentially reducing mortality rates. Moreover, integrating XAI techniques addresses the critical issue of model transparency, fostering greater trust and acceptance of AI-driven diagnostic tools in clinical settings.
How You Can Use This Info
Healthcare professionals and organizations can leverage the S-Net model to enhance cervical cancer screening processes, especially in environments with limited computational resources. Additionally, the use of XAI techniques can help medical practitioners understand and trust AI model predictions, aiding in better clinical decision-making. For technology developers, this research highlights the importance of creating lightweight and interpretable AI models that can be effectively deployed in real-world healthcare applications.