TY - JOUR
T1 - Aquila Optimizer with Bayesian Neural Network for Breast Cancer Detection on Ultrasound Images
AU - Obayya, Marwa
AU - Haj Hassine, Siwar Ben
AU - Alazwari, Sana
AU - K. Nour, Mohamed
AU - Mohamed, Abdullah
AU - Motwakel, Abdelwahed
AU - ISHFAQ YASEEN YASEEN, null
AU - ABU SARWAR ZAMANI, null
AU - Abdelmageed, Amgad Atta
AU - GOUSE PASHA MOHAMMED, null
N1 - Publisher Copyright:
© 2022 by the authors.
PY - 2022/9
Y1 - 2022/9
N2 - 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.
AB - 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.
KW - Aquila Optimizer
KW - Bayesian Neural Network
KW - breast cancer
KW - medical images
KW - ultrasound images
UR - http://www.scopus.com/inward/record.url?scp=85137843061&partnerID=8YFLogxK
U2 - 10.3390/app12178679
DO - 10.3390/app12178679
M3 - Article
AN - SCOPUS:85137843061
SN - 2076-3417
VL - 12
JO - Applied Sciences (Switzerland)
JF - Applied Sciences (Switzerland)
IS - 17
M1 - 8679
ER -