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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Kansal, K., Sharma, S. (2024). Predictive Deep Learning: An Analysis of Inception V3, VGG16, and VGG19 Models for Breast Cancer Detection. In: Garg, D., Rodrigues, J.J.P.C., Gupta, S.K., Cheng, X., Sarao, P., Patel, G.S. (eds) Advanced Computing. IACC 2023. Communications in Computer and Information Science, vol 2054. Springer, Cham. https://doi.org/10.1007/978-3-031-56703-2_28
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