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Hybrid deep learning and genetic algorithms approach (HMB-DLGAHA) for the early ultrasound diagnoses of breast cancer

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Abstract

Breast cancer is one of the most commonly diagnosed cancers in the world that has overtaken lung cancer and is considered a leading cause of molarity. The current study objectives are to (1) design an abstract CNN architecture named “HMB1-BUSI,” (2) suggest a hybrid deep learning and genetic algorithm approach for the learning and optimization named HMB-DLGAHA, (3) apply the transfer learning approach using pre-trained models, (4) study the effects of regularization, optimizers, dropout, and data augmentation through fourteen experiments, and (5) report the state-of-the-art performance metrics compared with other related studies and approaches. The dataset is collected and unified from two different sources (1) “Breast Ultrasound Images Dataset (Dataset BUSI)” and (2) “Breast Ultrasound Image.” The experiments implement the weighted sum (WS) method to judge the overall performance and generalization using loss, accuracy, F1-score, precision, recall, specificity, and area under curve (AUC) metrics with different ratios. MobileNet, MobileNetV2, InceptionResNetV2, DenseNet121, DenseNet169, DenseNet201, RestNet50, ResNet101, ResNet152, RestNet50V2, ResNet101V2, ResNet152V2, Xception, and VGG19 pre-trained CNN models are used in the experiments. Xception reported \(85.17\%\) as the highest WS metric. Xception, ResNet152V2, and ResNet101V2 reported accuracy and F1-score values above \(90\%\). Xception, ResNet152V2, ResNet101V2, and DenseNet169 reported precision values above \(90\%\). Xception and ResNet152V2 reported recall values above \(90\%\). All models unless ResNet152, ResNet50, and ResNet101 reported specificity values above \(90\%\) and unless ResNet152, ResNet50, ResNet101, and VGG19 reported AUC values above \(90\%\).

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Data Availability

The datasets, if existing, that are used, generated, or analyzed during the current study (A) if the datasets are owned by the authors, they are available from the corresponding author on reasonable request, (B) if the datasets are not owned by the authors, the supplementary information including the links and sizes are included in this published article.

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Authors and Affiliations

  1. Computers Engineering and Systems Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt

    Hossam Magdy Balaha

  2. Biomedical Engineering Program, Faculty of Engineering, Mansoura University, Mansoura, Egypt

    Mohamed Saif & Ahmed Tamer

  3. Electronics and Communications Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt

    Ehab H. Abdelhay

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1.1 Table of abbreviations

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Balaha, H.M., Saif, M., Tamer, A. et al. Hybrid deep learning and genetic algorithms approach (HMB-DLGAHA) for the early ultrasound diagnoses of breast cancer. Neural Comput & Applic 34, 8671–8695 (2022). https://doi.org/10.1007/s00521-021-06851-5

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