# Breast Lump Detection and Localization with a Tactile Glove Using Deep Learning

arXiv: https://arxiv.org/abs/2502.15767 · HTML: https://mlsanity.com/paper/2502.15767/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 50 references that are in the map.
- First posted on arXiv 15 Feb 2025.

## Builds on (most cited first, 5 of 5)

- [InceptionTime: Finding AlexNet for Time Series Classification](https://mlsanity.com/paper/1909.04939/) (2019)
- [Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline](https://mlsanity.com/paper/1611.06455/) (2016)
- [Multilevel Wavelet Decomposition Network for Interpretable Time Series Analysis](https://mlsanity.com/paper/1806.08946/) (2018)
- [Transfer learning for time series classification](https://mlsanity.com/paper/1811.01533/) (2018)
- [Forecasting new diseases in low-data settings using transfer learning](https://mlsanity.com/paper/2204.05059/) (2022)

## Built on by (most cited first, 2 of 2)

- [Toward Artificial Palpation: Representation Learning of Touch on Soft Bodies](https://mlsanity.com/paper/2511.16596/) (2025)
- [More with LESS -- Local Scene Representations for Tactile Imaging](https://mlsanity.com/paper/2606.14344/) (2026)

## Abstract (arXiv)

Breast cancer is the leading cause of mortality among women. Inspection of breasts by palpation is the key to early detection. We aim to create a wearable tactile glove that could localize the lump in breasts using deep learning (DL). In this work, we present our flexible fabric-based and soft wearable tactile glove for detecting the lumps within custom-made silicone breast prototypes (SBPs). SBPs are made of soft silicone that imitates the human skin and the inner part of the breast. Ball-shaped silicone tumors of 1.5-, 1.75- and 2.0-cm diameters are embedded inside to create another set with lumps. Our approach is based on the InceptionTime DL architecture with transfer learning between experienced and non-experienced users. We collected a dataset from 10 naive participants and one oncologist-mammologist palpating SBPs. We demonstrated that the DL model can classify lump presence, size and location with an accuracy of 82.22%, 67.08% and 62.63%, respectively. In addition, we showed that the model adapted to unseen experienced users with an accuracy of 95.01%, 88.54% and 82.98% for lump presence, size and location classification, respectively. This technology can assist inexperienced users or healthcare providers, thus facilitating more frequent routine checks.