An IoT-Enabled Deep Learning Approach Implemented on Android Device for Automated Identification of Breast Cancer Using Thermal Images

Abstract

Breast cancer (BC) is a very common type of cancer in women, and it occurs due to the abnormal growth of breast cells to produce malignant tumors. The early detection of BC is challenging in clinical standards to reduce the fatality rate caused by this disease. Artificial intelligence is helpful in early and automated detection of BC and provides a cost-effective way to assist radiologists in providing better diagnostic decisions. The artificial intelligence (AI) model integrated with the Internet of Things (IoT) provides the framework for real-time analysis of patient data and tele-healthcare monitoring for detecting BC. This paper proposes a novel IoT-enabled deep learning-based approach implemented on an Android device to detect BC using thermal images. A deep convolutional neural network (CNN) architecture with five blocks of cascaded convolutions followed by max-pooling after each block and cascaded dense layers is formulated and trained using the Google Cloud central processing unit. The post-training quantization (PTQ) of deep CNN (DPCNN) is performed using floating-point 16-bit (FP16) and integer 8-bit (INT 8)-based quantization techniques. The reduced-size DPCNN model is deployed on a cloud framework and an Android device for real-time detection of BC using thermal images. The DPCNN model deployed on the Android device provides a portable framework for low latency, enhanced privacy, and offline processing compared to the cloud-based framework for detecting BC using thermal images. The experimental results obtained using a public database reveal that the proposed DPCNN has obtained the accuracy values of 99.63% and 99.27% for FP16 and INT8 cases to detect BC. The proposed DPCNN model has fewer parameters and higher classification performance than transfer learning and existing methods in detecting BC using thermal images.

Keywords

breast cancer / thermal images / CNN / accuracy / Android implementation / IoT

References

[1] Gade, A., Dash, D. K., Kumari, T. M., Ghosh, S. K., Tripathy, R. K., & Pachori, R. B. (2023). Multiscale analysis domain interpretable deep neural network for detection of breast cancer using thermogram images. IEEE Transactions on Instrumentation and Measurement, 72, 1-13. https://doi.org/10.1109/TIM.2023.3317913

[2] World Health Organization. (2024). Breast cancer [Fact sheet]. Retrieved from: https://www.who.int/news-room/fact-sheets/detail/breast-cancer

[3] Kulothungan, V., Ramamoorthy, T., Sathishkumar, K., Mohan, R., Tomy, N., Miller, G. J., & Mathur, P. (2024). Burden of female breast cancer in India: Estimates of YLDs, YLLs, and DALYs at national and subnational levels based on the national cancer registry programme. Breast Cancer Research and Treatment, 205(2), 323-332. https://doi.org/10.1007/s10549-024-07264-3

[4] Dey, A., & Rajan, S. (2023). Hue—preserved quantile—based global contrast enhancement of breast thermograms for abnormality detection. IEEE Sensors Letters, 7(9), 1-4. https://doi.org/10.1109/LSENS.2023.3308087

[5] An, J., Abdus—Shakur, T., & Denis, M. (2023). Quantitative assessment of tissue stiffness using transfer learning ultrasound elastography: A breast cancer phantom study. IEEE Sensors Letters, 7(10), 1-4. https://doi.org/10.1109/LSENS.2023.3307102

[6] Singh, D., & Singh, A. K. (2020). Role of image thermography in early breast cancer detection—past, present and future. Computer Methods and Programs in Biomedicine, 183, 105074. https://doi.org/10.1016/j.cmpb.2019.105074

[7] Vinod, A., Guddati, P., Panda, A. K., & Tripathy, R. K. (2024). A lightweight deep convolutional neural network implemented on FPGA and Android devices for detection of breast cancer using ultrasound images. IEEE Access, 2, 179190-179203. https://doi.org/10.1109/ACCESS.2024.3506334

[8] Acharya, U. R., Ng, E. Y. K., Tan, J. H., & Sree, S. V. (2012). Thermography based breast cancer detection using texture features and support vector machine. Journal of Medical Systems, 36(3), 1503-1510. https://doi.org/10.1007/s10916-010-9611-z

[9] Milosevic, M., Jankovic, D., & Peulic, A. (2014). Thermography based breast cancer detection using texture features and minimum variance quantization. EXCLI Journal, 13, 1204-1215.

[10] Francis, S. V., Sasikala, M., & Saranya, S. (2014). Detection of breast abnormality from thermograms using curvelet transform based feature extraction. Journal of Medical Systems, 38(4), 23. https://doi.org/10.1007/s10916-014-0023-3

[11] Pramanik, S., Bhattacharjee, D., & Nasipuri, M. (2015). Wavelet based thermogram analysis for breast cancer detection. In 2015 International Symposium on Advanced Computing and Communication, 205-212. https://doi.org/10.1109/ISACC.2015.7377343

[12] EtehadTavakol, M., Chandran, V., Ng, E. Y. K., & Kafieh, R. (2013). Breast cancer detection from thermal images using bispectral invariant features. International Journal of Thermal Sciences, 69, 21-36. https://doi.org/10.1016/j.ijthermalsci.2013.03.001

[13] Zuluaga—Gomez, J., Al Masry, Z., Benaggoune, K., Meraghni, S., & Zerhouni, N. (2020). A CNN—based methodology for breast cancer diagnosis using thermal images. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 9(2), 131-145. https://doi.org/10.1080/21681163.2020.1824685

[14] Nissar, I., Alam, S., & Masood, S. (2024). Computationally efficient LC—SCS deep learning model for breast cancer classification using thermal imaging. Neural Computing and Applications, 36(26), 16233-16250. https://doi.org/10.1007/s00521-024-09968-5

[15] Chi Thanh, N., Thi Thu Hong, L., Doan, Q. T., & Taniar, D. (2025). A lightweight method for breast cancer detection using thermography images with optimized CNN feature and efficient classification. Journal of Imaging Informatics in Medicine, 38(3), 1434-1451. https://doi.org/10.1007/s10278-024-01269-6

[16] Guddati, P., Dash, S., & Tripathy, R. K. (2024). FPGA implementation of the proposed DCNN model for detection of tuberculosis and pneumonia using CXR images. IEEE Embedded Systems Letters, 16(4), 445-448. https://doi.org/10.1109/LES.2024.3370833

[17] Silva, L. F., Saade, D. C. M., Sequeiros, G. O., Silva, A. C., Paiva, A. C., Bravo, R. S., & Conci, A. (2014). A new database for breast research with infrared image. Journal of Medical Imaging and Health Informatics, 4(1), 92-100. https://doi.org/10.1166/jmihi.2014.1226

[18] Gonzalez, R. C. (2018). Deep convolutional neural networks [lecture notes]. IEEE Signal Processing Magazine, 35(6), 79-87. https://doi.org/10.1109/MSP.2018.2842646

[19] Bishop, C. M., & Bishop, H. (2024). Deep learning: Foundations and concepts (1st ed). Switzerland: Springer. https://doi.org/10.1007/978-3-031-45468-4

[20] Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L. C. (2018). MobileNetV2: Inverted residuals and linear bottlenecks. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 4510-4520. https://doi.org/10.1109/CVPR.2018.00474

[21] Tan, M., & Le, Q. V. (2021). EfficientNetV2: Smaller models and faster training. In Proceedings of the 38th International Conference on Machine Learning, 139, 10096-10106.

[22] Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large—scale image recognition. In 3rd International Conference on Learning Representations, 1-14.

[23] He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition, 770-778. https://doi.org/10.1109/CVPR.2016.90

[24] Shanmugamani, R. (2018). Deep learning for computer vision: Expert techniques to train advanced neural networks using TensorFlow and Keras. UK: Packt Publishing.

[25] Durmaz Incel, O., & Bursa, S. Ö. (2023). On—device deep learning for mobile and wearable sensing applications: A review. IEEE Sensors Journal, 23(6), 5501-5512. https://doi.org/10.1109/JSEN.2023.3240854

[26] Amin, M., Shehwar, D., Ullah, A., Guarda, T., Tanveer, T. A., & Anwar, S. (2022). A deep learning system for health care IoT and smartphone malware detection. Neural Computing and Applications, 34(14), 11283-11294. https://doi.org/10.1007/s00521-020-05429-x

[27] Su, L., & Lau, V. K. N. (2021). Hierarchical federated learning for hybrid data partitioning across multitype sensors. IEEE Internet of Things Journal, 8(13), 10922-10939. https://doi.org/10.1109/JIOT.2021.3051382