Title: Segmentation and classification of mammography images using DenseNet and VGG19 convolutional neural network

Authors: Suvashisa Dash; Raj Kumar Pattanaik; Mohammed Siddique; Sasmita Kumari Nayak; Bijay Kumar Paikaray; Satyasis Mishra

Addresses: Department of Mathematics, Centurion University of Technology and Management, Odisha, India ' Department of Mathematics, Centurion University of Technology and Management, Odisha, India ' Department of Mathematics, Centurion University of Technology and Management, Odisha, India ' Department of Computer Science and Engineering, Centurion University of Technology and Management, Odisha, India ' Centre for Data Science, Department of Computer Science and Engineering, Siksha 'O' Anusandhan (Deemed to be University), Odisha, India ' Department of ECE, Centurion University of Technology and Management, Odisha, India

Abstract: Breast cancer causes cancer death due to the abnormal growth in the thinning cell of breast. If the cancer not detected early, death rate will increase enormously. In recent years, machine learning and deep learning techniques have been used to classify images. In this study, an image classification from mammography can be achieved using DenseNet and VGG19 convolutional neural networks. We present the results of this study based on mammography images from digital database for screening mammography (DDSM). For the performance metric, we take into account precision, sensitivity, specificity, recall, and F1. DenseNet models achieve training, testing, and validation accuracy of 98.24%, 98.72%, and 97.85%, respectively, while VGG19 achieves 98.37%, 98.92%, and 97.94%. VGG19 performs better accuracy in comparison to DenseNet model. Moreover, the comparison results demonstrate the robustness of the proposed DenseNet and VGG19 models as fulfilling the sustainable development goal of 'good health and well-being'.

Keywords: convolution neural network; DenseNet; VGG19; deep learning; breast cancer; performance measure; benign and malignant.

DOI: 10.1504/IJIMS.2025.150840

International Journal of Internet Manufacturing and Services, 2025 Vol.11 No.4, pp.302 - 316

Received: 19 Feb 2024
Accepted: 16 Apr 2024
Published online: 24 Dec 2025 *

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