Smart Breast Cancer Detection: Enhancing Early Breast Cancer Diagnosis-Transitioning from Convolutional Neural Network to Involutional Neural Network and Using Smart Wearable Devices
Ayalew, Aleka Melese; Oussalah, Mourad; Abuhayi, Biniyam Mulugeta; Bezabh, Yohannes Agegnehu (2025-03-03)
Springer
03.03.2025
Ayalew, A.M., Oussalah, M., Abuhayi, B.M. et al. Smart Breast Cancer Detection: Enhancing Early Breast Cancer Diagnosis-Transitioning from Convolutional Neural Network to Involutional Neural Network and Using Smart Wearable Devices. Sens Imaging 26, 26 (2025). https://doi.org/10.1007/s11220-025-00561-1.
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© The Author(s) 2025. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
https://creativecommons.org/licenses/by/4.0/
© The Author(s) 2025. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202503132018
https://urn.fi/URN:NBN:fi:oulu-202503132018
Tiivistelmä
Abstract
Breast cancer is the most prevalent form of tumor in women, and this is the leading cause of mortality in women. Accurately detecting and classifying breast cancer is crucial for effective treatment and diagnosis preparation. Internet of Things (IoT) wearable devices (smart bra) are considered one of the best methods for early detection and, thereby, reducing breast cancer mortality. This study proposes a unique approach for breast cancer classification that combines involutional neural networks (InvNets) with wearable devices to reduce the parameter-intensive nature of convolutional neural networks. Specifically, the involution kernel differs from the convolution kernel because it is location-specific and channel-agnostic. This location-specific operation enhances the network's ability to acquire detailed elements in medical images by adapting to diverse visual patterns across spatial regions. In that regard, magnetic resonance imaging (MRI) has become an important modality for breast cancer detection. This work investigates the use of IoT devices such as wearables and sensors to collect patient health data and monitor changes in breast tissue. If symptoms are present, an MRI scan is performed for a reliable diagnosis of breast cancer utilizing deep learning (InvNets). Our findings show that InvNets achieves 100% training accuracy, 98.5% validation accuracy, and 98.6% testing accuracy after data augmentation, indicating its potential for breast cancer classification. InvNets are highly successful for medical image processing, particularly in circumstances with limited computer resources, as seen by enhanced accuracy and reduced parameter count. As a result, the findings show that InvNets provided consistent and reliable features for breast cancer detection and faster diagnosis.
Breast cancer is the most prevalent form of tumor in women, and this is the leading cause of mortality in women. Accurately detecting and classifying breast cancer is crucial for effective treatment and diagnosis preparation. Internet of Things (IoT) wearable devices (smart bra) are considered one of the best methods for early detection and, thereby, reducing breast cancer mortality. This study proposes a unique approach for breast cancer classification that combines involutional neural networks (InvNets) with wearable devices to reduce the parameter-intensive nature of convolutional neural networks. Specifically, the involution kernel differs from the convolution kernel because it is location-specific and channel-agnostic. This location-specific operation enhances the network's ability to acquire detailed elements in medical images by adapting to diverse visual patterns across spatial regions. In that regard, magnetic resonance imaging (MRI) has become an important modality for breast cancer detection. This work investigates the use of IoT devices such as wearables and sensors to collect patient health data and monitor changes in breast tissue. If symptoms are present, an MRI scan is performed for a reliable diagnosis of breast cancer utilizing deep learning (InvNets). Our findings show that InvNets achieves 100% training accuracy, 98.5% validation accuracy, and 98.6% testing accuracy after data augmentation, indicating its potential for breast cancer classification. InvNets are highly successful for medical image processing, particularly in circumstances with limited computer resources, as seen by enhanced accuracy and reduced parameter count. As a result, the findings show that InvNets provided consistent and reliable features for breast cancer detection and faster diagnosis.
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