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2024 | OriginalPaper | Buchkapitel

Predictive Deep Learning: An Analysis of Inception V3, VGG16, and VGG19 Models for Breast Cancer Detection

verfasst von : Kanika Kansal, Sanjiv Sharma

Erschienen in: Advanced Computing

Verlag: Springer Nature Switzerland

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Abstract

Breast cancer is a major contributor to cancer-related death in women. A higher likelihood of survival could result from early detection if the patient could receive the appropriate medicine while it is still in its early stages. Most often, a medical professional will use medical imaging or manual physical analysis to make a diagnosis. These efforts might be drastically cut with an automated approach. Using deep learning approaches, this paper proposes a system for autonomously analyzing ultrasound pictures. Using data obtained from the web repository Kaggle, three deep learning models—InceptionV3, VGG16, and VGG19—are applied to validate the suggested method. With the help of a confusion matrix and accuracy metrics, we compare the outcomes produced by these three deep learning methods. With an accuracy rate of 99.75%, the InceptionV3 model proved to be the most effective.

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Vorheriges Kapitel A Predictive Deep Learning Ensemble-Based Approach for Advanced Cancer Classification

Nächstes Kapitel Innovation in the Field of Oncology: Early Lung Cancer Detection and Classification Using AI

Mohammed, A., Kora, R.: A comprehensive review on ensemble deep learning: opportunities and challenges. J. King Saud Univ.-Comput. Inf. Sci. 35(2) (2023). https://doi.org/10.1016/j.jksuci.2023.01.014

Marrón-Esquivel, J.M., Duran-Lopez, L., Linares-Barranco, A., Dominguez-Morales, J.P.: A comparative study of the inter-observer variability on Gleason grading against Deep Learning-based approaches for prostate cancer. Comput. Biol. Med. 159, 106856 (2023). https://doi.org/10.1016/j.compbiomed.2023.106856

Khan, S., Islam, N., Jan, Z., Din, I.U., Rodrigues, J.J.C.: A novel deep learning based framework for the detection and classification of breast cancer using transfer learning. Pattern Recognit. Lett. 125 (2019). https://doi.org/10.1016/j.patrec.2019.03.022

Aslani, S., Jacob, J.: Utilisation of deep learning for COVID-19 diagnosis. Clin. Radiol. 78(2) (2023). https://doi.org/10.1016/j.crad.2022.11.006

Maleki, A., Raahemi, M., Nasiri, H.: Breast cancer diagnosis from histopathology images using deep neural network and XGBoost. Biomed. Signal Process. Control 86 (2023). https://doi.org/10.1016/j.bspc.2023.105152

Pineda, J., et al.: Geometric deep learning reveals the spatiotemporal features of microscopic motion. Nat. Mach. Intell. 5(1) (2023). https://doi.org/10.1038/s42256-022-00595-0

Gupta, S., Gupta, M.K.: A comparative analysis of deep learning approaches for predicting breast cancer survivability. Arch. Comput. Meth. Eng. 29(5) (2022). https://doi.org/10.1007/s11831-021-09679-3

Yadavendra, S.C.: A comparative study of breast cancer tumor classification by classical machine learning methods and deep learning method. Mach. Vis. Appl. 31(6) (2020). https://doi.org/10.1007/s00138-020-01094-1

Shahidi, F., Daud, S.M., Abas, H., Ahmad, N.A., Maarop, N.: Breast cancer classification using deep learning approaches and histopathology image: a comparison study. IEEE Access 8 (2020). https://doi.org/10.1109/ACCESS.2020.3029881

10.

Hamed, G., Marey, M.A.E.R., Amin, S.E. S., Tolba, M.F.: Deep learning in breast cancer detection and classification Adv. Intell. Syst. Comput. (2020)https://doi.org/10.1007/978-3-030-44289-7_30

11.

Parvin, F., Al Mehedi Hasan, M.: A comparative study of different types of convolutional neural networks for breast cancer histopathological image classification. In: 2020 IEEE Region 10 Symposium, TENSYMP 2020 (2020).https://doi.org/10.1109/TENSYMP50017.2020.9230787

12.

Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE Trans. Neural Netw. Learn. Syst. 33(12) (2022). https://doi.org/10.1109/TNNLS.2021.3084827

13.

Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights into Imaging 9(4) (2018). https://doi.org/10.1007/s13244-018-0639-9

14.

Acharya, U.R., et al.: A deep convolutional neural network model to classify heartbeats. Comput. Biol. Med. 89 (2017). https://doi.org/10.1016/j.compbiomed.2017.08.022

15.

“kaggle ultrasound” (2023) Accessed on 20 Aug 2023. https://www.kaggle.com/datasets/aryashah2k/breast-ultrasound-images-dataset

16.

Wang, C., et al.: Pulmonary image classification based on inception-v3 transfer learning model. IEEE Access 7 (2019). https://doi.org/10.1109/ACCESS.2019.2946000

17.

Mascarenhas, S., Agarwal, M.: A comparison between VGG16, VGG19 and ResNet50 architecture frameworks for Image Classification. In: Proceedings of IEEE International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications, CENTCON 2021 (2021). https://doi.org/10.1109/CENTCON52345.2021.9687944

18.

Qassim, H., Verma, A., Feinzimer, D.: Compressed residual-VGG16 CNN model for big data places image recognition. In: 2018 IEEE 8th Annual Computing and Communication Workshop and Conference, CCWC 2018 (2018).https://doi.org/10.1109/CCWC.2018.8301729

19.

Dey, N., Zhang, Y.D., Rajinikanth, V., Pugalenthi, R., Raja, N.S.M.: Customized VGG19 architecture for pneumonia detection in chest X-Rays. Pattern Recognit. Lett. 143 (2021). https://doi.org/10.1016/j.patrec.2020.12.010

Titel: Predictive Deep Learning: An Analysis of Inception V3, VGG16, and VGG19 Models for Breast Cancer Detection
verfasst von: Kanika Kansal
Sanjiv Sharma
Verlag: Springer Nature Switzerland
Buch: Advanced Computing
Print ISBN: 978-3-031-56702-5

Electronic ISBN: 978-3-031-56703-2

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-3-031-56703-2_28

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