Breast Cancer Detection on Mammographic Images using Hyper Parameter Tuning & Optimization: A Convolutional Neural Network & Transfer Learning Approach

Breast Cancer Detection on Mammographic Images using Hyper Parameter Tuning & Optimization: A Convolutional Neural Network & Transfer Learning Approach
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© 2022 by IJETT Journal
Volume-70 Issue-9
Year of Publication : 2022
Authors : Pratheep Kumar P, V. Mary Amala Bai
DOI : 10.14445/22315381/IJETT-V70I9P208

How to Cite?

Pratheep Kumar P, V. Mary Amala Bai, "Breast Cancer Detection on Mammographic Images using Hyper Parameter Tuning & Optimization: A Convolutional Neural Network & Transfer Learning Approach," International Journal of Engineering Trends and Technology, vol. 70, no. 9, pp. 76-92, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I9P208

Abstract
Breast cancer is probably the most well-known; it is also the leading cause of death in women worldwide. Incidentally, it can be cured if discovered early enough. The key issues for rural locations are that radiologists are significantly less likely to identify Breast Cancer (BC) utilizing mammography pictures in testing camps and that early diagnosis of these cancers is more important for all medical specialists. As a result, this study proposes an advanced deep learning-based tool for BC, with the following steps: a) Data Source: The Mammographic Image Analysis Society (MIAS) database of digital mammograms (v1.21), which contains 322 pictures and also real-world data from VPS Lakeshore Hospital Kochi which contains 4118 images, b) Filtering and histogram-based approach for preprocessing, c) feature extraction using a convolutional autoencoder for extracting features from the input, d) feature selection with Recursive Feature Elimination (RFE) for dimensionality reduction, and e) classification using a convolutional neural network with the help of transfer learning. Experiments are performed on various state-of-the-art models, and the suggested model outperforms in various measures (accuracy;0.96, precision;0.95, sensitivity;0.97, specificity:0.98).

Keywords
Breast Cancer, Classification, Convolutional Neural Network, Deep learning, Mammogram, Transfer Learning.

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