Hyperparameter Optimizer with Deep Learning-Based Decision-Support Systems for Histopathological Breast Cancer Diagnosis

Marwa Obayya; Mashael S Maashi; Nadhem Nemri; Heba Mohsen; Abdelwahed Motwakel; Azza Elneil Osman; Amani A Alneil; Mohamed Ibrahim Alsaid

doi:10.3390/cancers15030885

Hyperparameter Optimizer with Deep Learning-Based Decision-Support Systems for Histopathological Breast Cancer Diagnosis

Cancers (Basel). 2023 Jan 31;15(3):885. doi: 10.3390/cancers15030885.

Authors

Marwa Obayya¹, Mashael S Maashi², Nadhem Nemri³, Heba Mohsen⁴, Abdelwahed Motwakel⁵, Azza Elneil Osman⁶, Amani A Alneil⁶, Mohamed Ibrahim Alsaid⁶

Affiliations

¹ Department of Biomedical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
² Department of Software Engineering, College of Computer and Information Science, King Saud University, Riyadh 11543, Saudi Arabia.
³ Department of Information Systems, College of Science & Art at Mahayil, King Khalid University, Abha 62529, Saudi Arabia.
⁴ Department of Computer Science, Faculty of Computers and Information Technology, Future University in Egypt, New Cairo 11835, Egypt.
⁵ Department of Information Systems, College of Business Administration in Hawtat Bani Tamim, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia.
⁶ Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia.

Abstract

Histopathological images are commonly used imaging modalities for breast cancer. As manual analysis of histopathological images is difficult, automated tools utilizing artificial intelligence (AI) and deep learning (DL) methods should be modelled. The recent advancements in DL approaches will be helpful in establishing maximal image classification performance in numerous application zones. This study develops an arithmetic optimization algorithm with deep-learning-based histopathological breast cancer classification (AOADL-HBCC) technique for healthcare decision making. The AOADL-HBCC technique employs noise removal based on median filtering (MF) and a contrast enhancement process. In addition, the presented AOADL-HBCC technique applies an AOA with a SqueezeNet model to derive feature vectors. Finally, a deep belief network (DBN) classifier with an Adamax hyperparameter optimizer is applied for the breast cancer classification process. In order to exhibit the enhanced breast cancer classification results of the AOADL-HBCC methodology, this comparative study states that the AOADL-HBCC technique displays better performance than other recent methodologies, with a maximum accuracy of 96.77%.

Keywords: breast cancer classification; decision making; deep learning; healthcare; histopathological images.

Grants and funding

PNURSP2023R203/Princess Nourah bint Abdulrahman University