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Aquila Optimizer with Bayesian Neural Network for Breast Cancer Detection on Ultrasound Images

Authors :
Marwa Obayya
Siwar Ben Haj Hassine
Sana Alazwari
Mohamed K. Nour
Abdullah Mohamed
Abdelwahed Motwakel
Ishfaq Yaseen
Abu Sarwar Zamani
Amgad Atta Abdelmageed
Gouse Pasha Mohammed
Source :
Applied Sciences, Vol 12, Iss 17, p 8679 (2022)
Publication Year :
2022
Publisher :
MDPI AG, 2022.

Abstract

Breast cancer is the second most dominant kind of cancer among women. Breast Ultrasound images (BUI) are commonly employed for the detection and classification of abnormalities that exist in the breast. The ultrasound images are necessary to develop artificial intelligence (AI) enabled diagnostic support technologies. For improving the detection performance, Computer Aided Diagnosis (CAD) models are useful for breast cancer detection and classification. The current advancement of the deep learning (DL) model enables the detection and classification of breast cancer with the use of biomedical images. With this motivation, this article presents an Aquila Optimizer with Bayesian Neural Network for Breast Cancer Detection (AOBNN-BDNN) model on BUI. The presented AOBNN-BDNN model follows a series of processes to detect and classify breast cancer on BUI. To accomplish this, the AOBNN-BDNN model initially employs Wiener filtering (WF) related noise removal and U-Net segmentation as a pre-processing step. Besides, the SqueezeNet model derives a collection of feature vectors from the pre-processed image. Next, the BNN algorithm will be utilized to allocate appropriate class labels to the input images. Finally, the AO technique was exploited to fine-tune the parameters related to the BNN method so that the classification performance is improved. To validate the enhanced performance of the AOBNN-BDNN method, a wide experimental study is executed on benchmark datasets. A wide-ranging experimental analysis specified the enhancements of the AOBNN-BDNN method in recent techniques.

Details

Language :
English
ISSN :
20763417
Volume :
12
Issue :
17
Database :
Directory of Open Access Journals
Journal :
Applied Sciences
Publication Type :
Academic Journal
Accession number :
edsdoj.7c729594611480e8472b66e32623f38
Document Type :
article
Full Text :
https://doi.org/10.3390/app12178679