A Novel Hybrid Attention VGG Method For Benign and Malignant Breast Cancer Classification

Authors : Mustafa Salih Bahar

Pages : 27-47

Doi:10.24012/dumf.1502403

View : 87 | Download : 83

Publication Date : 2025-03-26

Article Type : Research Paper

Abstract :Worldwide, breast cancer is quite widespread among many types of cancer. Early detection is crucial for effective treatment. While early detection does not cure cancer or prevent its recurrence, it significantly improves treatment outcomes. Regular breast cancer check-ups, including mammograms, play a vital role in early detection. The type of the observed tumour is also crucial. Therefore, our study utilized a range of deep learning methods to accurately classify distinct forms of breast cancer cells, including both benign and malignant varieties. The problem addressed in the study relies on the classification of tumour images as either benign or malignant. We used the augmented MIAS and INBREAST datasets, implementing fourteen deep learning models by adjusting different hyperparameter values. Aside from these, we trained a new model we created, the Hybrid Attention VGG16 model, on the datasets by adjusting the batch size and learning rate values used in other models. Our research has shown that initially models like VGG16, VGG19, ResNet50, ResNet101, EfficientNetV2B0 and EfficientNetV2L performed better at different hyperparameter values, whereas our proposed model, the Hybrid Attention VGG model, achieved one of the highest performance among deep learning models across many hyperparameter values and on both datasets, especially on the Augmented INBREAST dataset. Our newly proposed model, with its unique skip connection and attention mechanism, surpasses the accuracy of models employed in earlier studies, as demonstrated when comparing them in the literature
Keywords : İyi Huylu Tümör, Kötü Huylu Tümör, Meme Kanseri Sınıflandırma, MIAS Mamografi Veriseti, Derin Öğrenme Modelleri