Journal of Machine and Computing


Integrated IoT-based Healthcare System for the Early Detection of Breast Cancer Using Intelligent Diagnostic System



Journal of Machine and Computing

Received On : 20 June 2022

Revised On : 06 October 2022

Accepted On : 10 December 2022

Published On : 05 January 2023

Volume 03, Issue 01

Pages : 027-037


Abstract


Breast cancer represents one of the leading cancer-related diseases worldwide, affecting mostly women after puberty. Even though the illness is fatal and kills thousands of people each year, it is mostly curative if found quickly. As a result, prompt and precise detection methods are critical to patient survival. Previously, doctors used manual detection systems for this objective. However, such techniques have been slow and frequently dependent on the physician's expertise. As technology advanced, these primitive methodologies were supplemented by computer-aided detection (CAD) algorithms. Deep learning is extremely common because of the massive development in large data, the Internet of Things (IoT), linked devices, and high-performance computers using GPUs and TPUs. The Internet of Things (IoT) has advanced recently, and the healthcare industry is benefiting from this growth. Sensors that gather data for required analysis are crucial components utilized in the Internet of Things. Physicians and medical staff will be able to carry out their tasks with ease and intelligence thanks to the Internet of Things. The proposed research focus on integrating Alexnet and ResNet101 for accurate prediction of Breast malignancy from mammogram data. This methodology will target the features more precisely than any other combination of the pre-trained model. Finally, to resolve the computational burden issue, the feature reduction ReliefF methodology is used. To demonstrate the proposed method, an online publicly released set of data of 750 BU images is used. For training and testing the models, the set of data has been further split into 80 and 20% ratios. Following extensive testing and analysis, it was discovered that the DenseNet-201 and MobileNet-v2 trained SVMs to have an accuracy of 98.39 percent for the original and augmented Mammo images online datasets, respectively. This research discovered that the proposed approach is efficient and simple to implement to assist radiographers and physicians in diagnosing breast cancer in females.


Keywords


Internet of Things, Sensors, Convolutional Neural Network, Mammogram, Alexnet, Inception, Robustness, Integration, Malignancy, Intelligent Diagnostic System.


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Acknowledgements


The authors would like to thank to the reviewers for nice comments on the manuscript.


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Cite this article


Shruthishree S H, Harshvardhan Tiwari, Devaraj Verma, “Integrated IoT-based Healthcare System for the Early Detection of Breast Cancer Using Intelligent Diagnostic System”, Journal of Machine and Computing, pp. 027-037, January 2023. doi: 10.53759/7669/jmc202303004.


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© 2023 Shruthishree S H, Harshvardhan Tiwari, Devaraj Verma. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.