Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Aug;36(4):1489-1506.
doi: 10.1007/s10278-023-00834-9. Epub 2023 May 23.

Smart IoT in Breast Cancer Detection Using Optimal Deep Learning

Ramachandro Majji  1 , Om Prakash P G  2 , R Rajeswari  3 , Cristin R  4
Affiliations

Smart IoT in Breast Cancer Detection Using Optimal Deep Learning

Ramachandro Majji et al. J Digit Imaging. 2023 Aug.

Abstract

IoT in healthcare systems is currently a viable option for providing higher-quality medical care for contemporary e-healthcare. Using an Internet of Things (IoT)-based smart healthcare system, a trustworthy breast cancer classification method called Feedback Artificial Crow Search (FACS)-based Shepherd Convolutional Neural Network (ShCNN) is developed in this research. To choose the best routes, the secure routing operation is first carried out using the recommended FACS while taking fitness measures such as distance, energy, link quality, and latency into account. Then, by merging the Crow Search Algorithm (CSA) and Feedback Artificial Tree, the produced FACS is put into practice (FAT). After the completion of routing phase, the breast cancer categorization process is started at the base station. The feature extraction step is then introduced to the pre-processed input mammography image. As a result, it is possible to successfully get features including area, mean, variance, energy, contrast, correlation, skewness, homogeneity, Gray Level Co-occurrence Matrix (GLCM), and Local Gabor Binary Pattern (LGBP). The quality of the image is next enhanced through data augmentation, and finally, the developed FACS algorithm's ShCNN is used to classify breast cancer. The performance of FACS-based ShCNN is examined using six metrics, including energy, delay, accuracy, sensitivity, specificity, and True Positive Rate (TPR), with the maximum energy of 0.562 J, the least delay of 0.452 s, the highest accuracy of 91.56%, the higher sensitivity of 96.10%, the highest specificity of 91.80%, and the maximum TPR of 99.45%.

Keywords: And Shepherd Convolutional Neural Network; Breast cancer; Crow Search Algorithm; Feedback Artificial Tree; Internet of Things.

PubMed Disclaimer

Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
System model of IoT
Fig. 2
Fig. 2
Block diagram of breast cancer detection based on proposed FACS-based ShCNN in IoT system
Fig. 3
Fig. 3
Solution encoding
Fig. 4
Fig. 4
Architecture of ShCNN
Fig. 5
Fig. 5
Experimental outcomes of developed FACS-based ShCNN. a Input image of dataset-1, b pre-processed result based on ROI extraction of dataset-1, c input image of dataset-2, d pre-processed result based on ROI extraction of dataset-2
Fig. 6
Fig. 6
Comparative assessment of the developed technique using dataset-1 based on a energy, b delay, c accuracy, d sensitivity, e specificity, f TPR
Fig. 7
Fig. 7
Comparative assessment of developed technique using dataset-2 based on a energy, b delay, c accuracy, d sensitivity, e specificity, f TPR
See this image and copyright information in PMC

References

    1. Shende, Dipali K. and Yogesh S. Angal, and S.C. Patil. “An Iterative CrowWhale-Based Optimization Model for Energy-Aware Multicast Routing in IoT,” International Journal of Information Security and Privacy (IJISP) vol.16, no.1, pp.1–24, 2022.
    1. Shende, Dipali & Sonavane, Shefali & Angal, Yogesh, “A Comprehensive Survey of the Routing Schemes for IoT applications”, Scalable Computing: Practice and Experience, vol.no. 21, pp.203–216, 2020.
    1. Jaglan, P., Dass, R. and Duhan, M., “Breast cancer detection techniques: issues and challenges”, Journal of The Institution of Engineers (India): Series B, vol.100, no.4, pp.379–386, August 2019.
    1. K. Al-Saedi, M. Al-Emran, E. Abusham and S. A. El Rahman, “Mobile Payment Adoption: A Systematic Review of the UTAUT Model,” 2019 International Conference on Fourth Industrial Revolution (ICFIR), 2019, pp. 1–5,2019.
    1. V. Rupapara, M. Narra, N. K. Gunda, S. Gandhi and K. R. Thipparthy, “Maintaining Social Distancing in Pandemic Using Smartphones With Acoustic Waves,” in IEEE Transactions on Computational Social Systems, vol. 9, no. 2, pp. 605–611, 2022,