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.
J. Tang, R. M. Rangayyan, J. Xu, I. E. Naqa and Y. Yang, “Computer Aided Detection and Diagnosis of Breast Cancer with Mammography: Recent Advances”, IEEE Journal of and Health Informatics 13 (2): 236251, 2009.
G. E. Hinton, S. Osindero and Y. W. The “A fast learning algorithm for deep belief nets.” Journal of Neural Computation 18 (7): 15271554, 2006.
Basheer, S., Anbarasi, M., Sakshi, D.G. and Vinoth Kumar V., “Efficient text summarization method for blind people using text mining techniques”, Int J Speech Technol, 23, 713–725 (2020). Doi.10.1007/s10772-020-09712-z
Olga Russakovsky, Jia Deng, Hao Su, “ImageNet Large Scale Visual Recognition Challenge”, International Journal of Computer Vision 115 (3): 211252, 2005.
Y. Lecun, L. Bottou, Y. Bengio and P. Haffner, “Gradient based learning applied to document recognition”, Proceedings of the IEEE 86 (11), 1998.
Kouser, R. R., Manikandan, T., & Kumar, V. V., “Heart Disease Prediction System Using Artificial Neural Network, Radial Basis Function and Case Based Reasoning”, Journal of Computational and Theoretical Nanoscience, 15(9), 2810–2817, 2018. Doi.10.1166/jctn.2018.7543
M. Radovic, O. Adarkwa, and Q.Wang, “Object Recognition in Aerial Images Using Convolutional Neural Networks”, Journal of Imaging 3 (2), 21, 2017.
J. Long, E. Shelhamer and T. Darrell, “Fully convolutional networks for semantic segmentation”, IEEE Journal of Transactions on Pattern Analysis and Machine Intelligence 39 (4), 2017.
Ahmed, S. T., Kumar, V. V., Singh, K. K., Singh, A., Muthukumaran, V., & Gupta, D., “6G enabled federated learning for secure IoMT resource recommendation and propagation analysis. Computers and Electrical Engineering, 102, 108210, 2022. Doi.10.1016/j.compeleceng.2022.108210
T.Kooi., “Large scale deep learning for computer aided detection of mammographic lesions”, Med. Image Anal. 35, 303–312 (2017).
A. Akselrod-Ballin, “A region based convolutional network for tumor detection and classification in breast mammography”, International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, pp. 197–205, 2016.
Sarah S. Aboutalid, “Deep Learning to Distinguish Recalled but Benign Mammography Images in Breast Cancer Screening”. In: Clinical Cancer Research, 1-11, 2018.
K. Kira and L. A. Rendell, “A practical approach to feature selection,” in Machine Learning Proceedings, pp. 249–256, 1992.
R. J. Urbanowicz, M. Meeker, W. La Cava, R. S. Olson, and J. H. Moore, “Relief-based feature selection: introduction and review,” Journal of Biomedical Informatics, vol. 85, pp. 189–203, 2018.
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Shruthishree S H
Shruthishree S H
Department of Information Science and Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Bangalore, India.
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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.
