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

TL;DR

This paper introduces a wearable tactile glove utilizing deep learning to detect and localize breast lumps in silicone prototypes, demonstrating promising accuracy and adaptability for aiding early breast cancer detection.

Contribution

The study presents a novel fabric-based tactile glove with deep learning for lump detection, including transfer learning to adapt to different users, advancing assistive breast examination technology.

Findings

Achieved 82.22% accuracy in lump presence detection.

Demonstrated 67.08% accuracy in lump size classification.

Model adapted to new users with over 82% accuracy.

Abstract

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.