An optimized model based on adaptive convolutional neural network and grey wolf algorithm for breast cancer diagnosis.

Autor: Alnowaiser K; College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al Kharj, Saudi Arabia., Saber A; Information Technology Department, Faculty of Computers and Artificial Intelligence, Damietta University, Damietta, Egypt., Hassan E; Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh, Egypt., Awad WA; Computer Science Department, Faculty of Computers and Artificial Intelligence, Damietta University, Damietta, Egypt.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2024 Aug 19; Vol. 19 (8), pp. e0304868. Date of Electronic Publication: 2024 Aug 19 (Print Publication: 2024).
DOI: 10.1371/journal.pone.0304868
Abstrakt: Medical image classification (IC) is a method for categorizing images according to the appropriate pathological stage. It is a crucial stage in computer-aided diagnosis (CAD) systems, which were created to help radiologists with reading and analyzing medical images as well as with the early detection of tumors and other disorders. The use of convolutional neural network (CNN) models in the medical industry has recently increased, and they achieve great results at IC, particularly in terms of high performance and robustness. The proposed method uses pre-trained models such as Dense Convolutional Network (DenseNet)-121 and Visual Geometry Group (VGG)-16 as feature extractor networks, bidirectional long short-term memory (BiLSTM) layers for temporal feature extraction, and the Support Vector Machine (SVM) and Random Forest (RF) algorithms to perform classification. For improved performance, the selected pre-trained CNN hyperparameters have been optimized using a modified grey wolf optimization method. The experimental analysis for the presented model on the Mammographic Image Analysis Society (MIAS) dataset shows that the VGG16 model is powerful for BC classification with overall accuracy, sensitivity, specificity, precision, and area under the ROC curve (AUC) of 99.86%, 99.9%, 99.7%, 97.1%, and 1.0, respectively, on the MIAS dataset and 99.4%, 99.03%, 99.2%, 97.4%, and 1.0, respectively, on the INbreast dataset.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2024 Alnowaiser et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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