Transfer Learning Based Breast Cancer Detection and Classification using Mammogram Images
R Remya, Narayanan Hema Rajini · 2022 International Conference on Electronics and Renewable Systems (ICEARS) · 2022
Breast cancer can be considered a deadly disease affecting women over the globe. Since severity of breast cancer is high at advanced stages, early detection processes find useful to increase the survival rate. The recently presented medical imaging modalities and deep learning (DL) models pave the way to design effective breast cancer, classification models. In this view, this study develops a new deep transfer learning enabled breast cancer detection and classification (DTL-BCDC) model using mammogram images. The proposed DTL-BDCD technique mainly intends to identify the presence of breast cancer. Primarily, the DTL-BDCD model involves contrast enhancement using CLAHE technique and adaptive weighted segmentation (AWS) technique is used for determining the infected regions. Besides, Densely Connected Networks (DenseNet-169) model is employed for feature extraction and multilayer perceptron (MLP) is utilized for breast cancer classification. The design of DenseNet169 with MLP model for breast cancer detection shows the novelty of the work. In order to demonstrate the enhanced outcomes of the DTL-BDCD model, a wide range of simulations take place on benchmark datasets and the comparison study reported the betterment of the DTL-BDCD model over the recent approaches.