Breast Lump Detection and Localization with a Tactile Glove Using Deep Learning
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915172049747968 |
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| author | Syrymova, Togzhan Yelenov, Amir Burunchina, Karina Abulkhanova, Nazgul Varol, Huseyin Atakan Ramon, Juan Antonio Corrales Kappassov, Zhanat |
| author_facet | Syrymova, Togzhan Yelenov, Amir Burunchina, Karina Abulkhanova, Nazgul Varol, Huseyin Atakan Ramon, Juan Antonio Corrales Kappassov, Zhanat |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_15767 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Breast Lump Detection and Localization with a Tactile Glove Using Deep Learning Syrymova, Togzhan Yelenov, Amir Burunchina, Karina Abulkhanova, Nazgul Varol, Huseyin Atakan Ramon, Juan Antonio Corrales Kappassov, Zhanat Quantitative Methods Computer Vision and Pattern Recognition Image and Video Processing 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. |
| title | Breast Lump Detection and Localization with a Tactile Glove Using Deep Learning |
| topic | Quantitative Methods Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2502.15767 |