Computer-aided diagnosis for breast cancer detection and classification using optimal region growing segmentation with MobileNet model. (June 2022)

• Record Type: Journal Article
• Authors: Dafni Rose, J; VijayaKumar, K; Singh, Laxman; Sharma, Sudhir Kumar
• Is Part Of: Concurrent engineering, research and applications. Volume 30:Number 2(2022)
• Page Start: 181
• Page End: 189
• Publication Date: 2022-06
• Subjects: breast cancer -- computer aided diagnosis -- digital mammograms -- segmentation -- parameter optimization -- deep learning -- metaheuristics; Production engineering -- Periodicals; Concurrent engineering -- Periodicals; 621.39
• DOI: 10.1177/1063293X221080518
• Languages: English
• ISSNs: 1063-293X

Abstract

Globally, breast cancer is considered a major reason for women's morality. Earlier and accurate identification of breast cancer is essential to increase survival rates. Therefore, computer-aided diagnosis (CAD) models are developed to help radiologists in the detection of mammographic lesions. Presently, machine-learning (ML) and deep-learning (DL) models are widely employed in the disease diagnostic process. In this view, this paper designs a novel CAD using optimal region growing segmentation with a MobileNet (CAD-ORGSMN) model for breast cancer identification and classification. The proposed CAD-ORGSMN model involves different stages of operations, namely, pre-processing, segmentation, feature extraction, and classification. Primarily, the proposed model uses a Weiner filtering (WF)–based pre-processing technique to remove the existence of noise in the mammogram images. The CAD-ORGSMN model involves a glowworm swarm optimization (GSO)–based region growing technique for image segmentation where the initial seed points and threshold values are optimally created by the GSO algorithm. Besides, a MobileNet-based feature extractor is used in which the hyperparameters of the MobileNet model are optimally selected using a swallow swarm optimization (SSO) algorithm. Lastly, variational autoencoder is applied as a classifier to determine the class labels for the input mammogram images. The utilization of the GSO algorithm for the region growing technique and the SSO algorithm for hyperparameter optimization helps to considerably improve the breast cancer detection performance of the CAD-ORGSMN model. The performance validation of the CAD-ORGSMN model takes place against the Mini-MIAS database, and the obtained results highlighted the promising performance of the CAD-ORGSMN model over the recent state-of-the-art methods in terms of different measures.