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. 2024 Aug 19;19(8):e0304868.
doi: 10.1371/journal.pone.0304868. eCollection 2024.

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

Khaled Alnowaiser  1 , Abeer Saber  2 , Esraa Hassan  3 , Wael A Awad  4
Affiliations

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

Khaled Alnowaiser et al. PLoS One. .

Abstract

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.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Shows the proposed model steps.
Fig 2
Fig 2. Shows the data pre-processing steps.
Fig 3
Fig 3
(a), depicts the construction of the VGG-16. (b), Illustrates the structure of the DenseNet-121.
Fig 4
Fig 4. Shows the frequency of the most popular datasets that applied in BC classification.
Fig 5
Fig 5. Describes the classes in the MIAS dataset.
Fig 6
Fig 6. Describes the INbreast dataset classes.
See this image and copyright information in PMC

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