Breast cancer remains a predominant cause of mortality among women, highlighting the importance of accurate early detection. Mammography can be hampered by breast density and radiation exposure, while ultrasound is safer and more accessible. However, ultrasound images have speckle noise, low contrast, and often blurred lesion boundaries, making readings subjective. Transfer learning-based Convolutional Neural Network (CNN) approaches are widely used, but structured comparisons of architectures for three-class breast cancer classification (benign, malignant, and normal) on the combined BUSI and Mendeley datasets, particularly with respect to computational efficiency, remain limited. This study evaluates five transfer learning models (VGG16, VGG19, InceptionV3, Xception, ResNet50) on 1,030 images from BUSI and Mendeley. The images were standardised to PNG, converted to RGB, resized to 224×224, noise-reduced with a 3×3 median filter, normalised according to the architecture preprocessing, and augmented conservatively. The dataset was split into an 80% training set, a 10% validation set, and a 10% testing set, utilizing ImageNet weights and implementing partial fine-tuning. ResNet50 exhibited superior performance, attaining an accuracy of 90.29%, a precision of 90.48%, a recall of 90.29%, and an F1-score of 88.74%, outperforming VGG19 (F1-score 87.58%) and Xception (F1-score 86.51%). These findings suggest that computationally efficient models can deliver reliable results and may support computer-aided ultrasound diagnosis in resource-limited environments.
Gena Darma, Made Naradeon, Handika Pramesta et al.· International Conference on...· 0 citations
Breast cancer is still one of the most common cancers worldwide, with about 2.26 million new cases in 2020. Ultrasound (USG) images are an important screening method because they are non-invasive and affordable. However, USG images often have speckle noise and low contrast, which can make diagnosis less consistent. While deep learning has significantly improved classification accuracy, many complex models require substantial computational resources, limiting their application on portable or edge devices. Furthermore, the opaque nature of these models often hinders clinical trust and interpretability. In this study, we propose a precise and interpretable classification system for breast ultrasound images using a combined dataset of 1,030 samples from the BUSI and Mendeley repositories. We evaluated six distinct deep learning architectures: DenseNet121, EfficientNetB3, EfficientNetB4, MobileNetV2, MobileNetV3, and VGG16. To address the black box nature of deep learning, we integrated the Grad-CAM technique, which generates visual saliency maps to highlight relevant pathological features. Our experiments showed that MobileNetV3 had the best overall performance, with 92.41% accuracy and a 91.19% F1-score. EfficientNetB3 had the highest sensitivity, with a recall of 96.98%. Grad-CAM visualizations showed that the models focused on the important areas of the lesions. These results suggest that using lightweight models with Explainable AI (XAI) can offer an effective, reliable, and efficient diagnostic tool for real-world clinical use, especially where resources are limited.
Muhammad Bintang Prajudha, Ihab Hasanain Akmal, Ardhian Calwa Nugraha et al.· International Conference on...· 0 citations
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