Deep Learning-based Breast Cancer Diagnosis Method using Convolutional Neural Networks (CNNs)
DOI:
https://doi.org/10.67603/jaate.v1i02.10443Keywords:
Breast cancer, transfer learning, DenseNet121, machine learning, classificationAbstract
Breast cancer (BC) remains a significant global health concern, contributing to one of the highest mortality rates among women. Early and accurate detection is paramount for improving survival rates and ensuring a healthier life. In recent years, computing and information technology advancements have revolutionized healthcare systems, particularly in disease detection and classification. This work introduces a hybrid method to enhance breast cancer classification by combining deep learning-based techniques like convolutional neural networks (CNNs) and machine learning-based classifiers. We leveraged several well-known pre-trained CNNs, including ResNet50, MobileNetV2, DenseNet121, and Xception, to capture pertinent features of breast ultrasound images and effectively catch the intricate patterns associated with malignant and benign tumors. Subsequently, these extracted features were fed into high-performance classifiers such as SVM, KNN, XGBoost, and Softmax to accurately classify breast cancer based on the well-known benchmark, the Breast Ultrasound Images (BUSI) dataset. The findings demonstrated the efficacy of our proposed method, with the DenseNet121 network, when combined with the Softmax classifier, achieving the most superior performance with the highest accuracy of 95.34% compared to other models and state-of-the-art techniques, demonstrating the robustness and reliability of the introduced method in distinguishing between malignant and benign breast tumors, thereby facilitating more informed clinical decision making.
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