A hybrid end-to-end learning approach for breast cancer diagnosis: convolutional recurrent network. (January 2023)

• Record Type: Journal Article
• Authors: Aslan, Muhammet Fatih
• Is Part Of: Computers & electrical engineering. Volume 105(2023)
• Publication Date: 2023-01
• Subjects: BiLSTM -- Breast cancer -- CNN -- Deep learning -- End-to-end learning -- Hybrid architecture -- Image preprocessing -- Mammography; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.108562
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: A new proposition of hybrid architecture for breast cancer detection, Diagnosis for cancer detection without the need for region of interest (ROI) information, Breast cancer diagnosis with high classification accuracy, The proposed method is easy and high performance. Abstract: In this study, mammography images are classified as normal, benign, and malignant using the Mammographic Image Analysis Society (MIAS) and INbreast datasets. After the preprocessing of each image, the processed images are given as input to two different end-to-end deep networks. The first network contains only a Convolutional Neural Network (CNN), while the second network is a hybrid structure that includes both the CNN and Bidirectional Long Short Term Memories (BiLSTM). The classification accuracy obtained using the first and second hybrid architectures is 97.60% and 98.56% for the MIAS dataset, respectively. In addition, experiments performed for the INbreast dataset at the study's end prove the proposed method's effectiveness. These results are comparable to those obtained in previous popular studies. The proposed study contributes to previous studies in terms of preprocessing steps, deep network design, and high diagnostic accuracy.