# Breast Cancer Segmentation using Attention-based Convolutional Network and Explainable AI

arXiv: https://arxiv.org/abs/2305.14389 · HTML: https://mlsanity.com/paper/2305.14389/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 16 references that are in the map.
- First posted on arXiv 22 May 2023.

## Builds on (most cited first, 4 of 4)

- [YOLO5Face: Why Reinventing a Face Detector](https://mlsanity.com/paper/2105.12931/) (2021)
- [Bias Remediation in Driver Drowsiness Detection systems using Generative Adversarial Networks](https://mlsanity.com/paper/1912.12123/) (2019)
- [Video Vision Transformers for Violence Detection](https://mlsanity.com/paper/2209.03561/) (2022)
- [LesionAid: Vision Transformers-based Skin Lesion Generation and Classification](https://mlsanity.com/paper/2302.01104/) (2023)

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

- [Breast Cancer Classification with Enhanced Interpretability: DALAResNet50 and DT Grad-CAM](https://mlsanity.com/paper/2308.13150/) (2023)

## Abstract (arXiv)

Breast cancer (BC) remains a significant health threat, with no long-term cure currently available. Early detection is crucial, yet mammography interpretation is hindered by high false positives and negatives. With BC incidence projected to surpass lung cancer, improving early detection methods is vital. Thermography, using high-resolution infrared cameras, offers promise, especially when combined with artificial intelligence (AI). This work presents an attention-based convolutional neural network for segmentation, providing increased speed and precision in BC detection and classification. The system enhances images and performs cancer segmentation with explainable AI. We propose a transformer-attention-based convolutional architecture (UNet) for fault identification and employ Gradient-weighted Class Activation Mapping (Grad-CAM) to analyze areas of bias and weakness in the UNet architecture with IRT images. The superiority of our proposed framework is confirmed when compared with existing deep learning frameworks.