# Block-Fused Attention-Driven Adaptively-Pooled ResNet Model for Improved Cervical Cancer Classification

arXiv: https://arxiv.org/abs/2405.01600 · HTML: https://mlsanity.com/paper/2405.01600/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 84 references that are in the map.
- First posted on arXiv 1 May 2024.

## Builds on (most cited first, 3 of 3)

- [Interpretable Deep Learning: Interpretation, Interpretability, Trustworthiness, and Beyond](https://mlsanity.com/paper/2103.10689/) (2021)
- [Classifier Calibration: A survey on how to assess and improve predicted class probabilities](https://mlsanity.com/paper/2112.10327/) (2021)
- [Multi-Task Driven Explainable Diagnosis of COVID-19 using Chest X-ray Images](https://mlsanity.com/paper/2008.03205/) (2020)

## Built on by (most cited first, 1 of 1)

- [An Improved Adaptive PID Optimizer with Enhanced Convergence and Stability for Deep Learning](https://mlsanity.com/paper/2605.21968/) (2026)

## Abstract (arXiv)

Cervical cancer is the second most common cancer among women and a leading cause of mortality. Many attempts have been made to develop an effective Computer Aided Diagnosis (CAD) system; however, their performance remains limited. Using pretrained ResNet-50/101/152, we propose a novel CAD system that significantly outperforms prior approaches. Our novel model has three key components. First, we extract detailed features (color, edges, and texture) from early convolution blocks and the abstract features (shapes and objects) from later blocks, as both are equally important. This dual-level feature extraction is a new paradigm in cancer classification. Second, a non-parametric 3D attention module is uniquely embedded within each block for feature enhancement. Third, we design a theoretically motivated innovative adaptive pooling strategy for feature selection that applies Global Max Pooling to detailed features and Global Average Pooling to abstract features. These components form our Proposed Block-Fused Attention-Driven Adaptively-Pooled ResNet (BF-AD-AP-ResNet) model. To further strengthen learning, we introduce a Tri-Stream model, which unifies the enhanced features from three BF-AD-AP-ResNets. An SVM classifier is employed for final classification. We evaluate our models on two public datasets, IARC and AnnoCerv. On IARC, the base ResNets achieve an average performance of 90.91%, while our model achieves an excellent performance of 98.63%. On AnnoCerv, the base ResNets reach to 87.68%, and our model improves this significantly, reaching 93.39%. Our approach outperforms the best existing method on IARC by an average of 14.55%. For AnnoCerv, no prior competitive works are available. Additionally, we introduce a novel SHAP+LIME explainability method, accurately identifying the cancerous region in 97% of cases, ensuring model reliability for real-world use.