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BRMI-Net: Deep Learning Features and Flower Pollination-Controlled Regula Falsi-Based Feature Selection Framework for Breast Cancer Recognition in Mammography Images. Diagnostics 2023, 13, 1618 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)diagnostics13091618  \nAcademic Editors: Sameer Antani and Dechang Chen  \nReceived: 27 February 2023  \nRevised: 16 April 2023  \nAccepted: 26 April 2023  \nPublished: 3 May 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Computer Science, HITEC University, Taxila 47080, Pakistan  \n2 Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway  \n3 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia; [naalmujally@pnu.edu.sa](naalmujally@pnu.edu.sa)  \n4 College of Computer Science, King Khalid University, Abha 61413, Saudi Arabia; [jabaili@kku.edu.sa](jabaili@kku.edu.sa)  \n5 College of Computer Science and Engineering, University of Ha'il, Ha'il 81451, Saudi Arabia; [m.alhaisoni@uoh.edu.sa](m.alhaisoni@uoh.edu.sa)  \n6 Management Information System Department, College of Business Administration, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia; [u.tariq@psau.edu.sa](u.tariq@psau.edu.sa)  \n7 Department of Informatics, University of Leicester, Leicester LE1 7RH, UK; [yudong.zhang@leicester.ac.uk](yudong.zhang@leicester.ac.uk)  \n* Correspondence: [anum.masood@ntnu.no](anum.masood@ntnu.no)  \nAbstract: The early detection of breast cancer using mammogram images is critical for lowering women's mortality rates and allowing for proper treatment. Deep learning techniques are commonly used for feature extraction and have demonstrated significant performance in the literature. However, these features do not perform well in several cases due to redundant and irrelevant information. We created a new framework for diagnosing breast cancer using entropy-controlled deep learning and flower pollination optimization from the mammogram images. In the proposed framework, a filter fusion-based method for contrast enhancement is developed. The pre-trained ResNet-50 model is then improved and trained using transfer learning on both the original and enhanced datasets. Deep features are extracted and combined into a single vector in the following phase using a serial technique known as serial mid-value features. The top features are then classified using neural networks and machine learning classifiers in the following stage. To accomplish this, a technique for flower pollination optimization with entropy control has been developed. The exercise used three publicly available datasets: CBIS-DDSM, INbreast, and MIAS. On these selected datasets, the proposed framework achieved 93.8, 99.5, and 99.8% accuracy, respectively. Compared to the current methods, the increase in accuracy and decrease in computational time are explained.  \nKeywords: breast cancer; data augmentation; deep learning; features fusion; feature optimization  \n1. Introduction  \nBreast cancer is the most co","cbCailmvnjyeFGo8","https://ap.wps.com/l/cbCailmvnjyeFGo8","pdf",4701277,24,"English","# Abstract\n# Introduction\n## Breast cancer background and need for early detection\n## Imaging methods and role of mammography\n# Methods (BRMI-Net overview)","[{\"question\":\"Why is feature selection important for breast cancer recognition in mammography images?\",\"answer\":\"Deep learning feature extraction can suffer when features include redundant and irrelevant information, which reduces classification performance. The framework addresses this by optimizing features before classification.\"},{\"question\":\"What major components does the proposed BRMI-Net framework include?\",\"answer\":\"BRMI-Net uses filter-fusion contrast enhancement, transfer learning with a ResNet-50 model on original and enhanced datasets, serial mid-value feature extraction, and then classification using neural networks and machine-learning classifiers.\"},{\"question\":\"How was the framework evaluated and what performance was reported?\",\"answer\":\"The method was tested on three publicly available datasets: CBIS-DDSM, INbreast, and MIAS. Reported accuracies are 93.8%, 99.5%, and 99.8%, respectively, with claimed improvements in accuracy and reduced computational time versus current methods.\"}]","BRMI-Net - Deep Learning Features and Flower Pollination-Controlled Regula Falsi-Based Feature Selection Framework for Breast Cancer Recognition in Mammography Images | PDF",1789643015]