Performance Assessment of Fourier Convolutional Neural Networks in Medical Image Analysis for Breast Cancer Diagnosis
Songkiat Lowmunkhong, Anan Panphuech, Patthra Janthawanno, Ratapong Onjun, Sayan Kaennakham · Frontiers in artificial intelligence and applications · 2024
Breast cancer remains a major global health concern, and early diagnosis is crucial for improving patient outcomes. This study explores the performance of Fourier Convolutional Neural Networks (FCNNs) in comparison to traditional Convolutional Neural Networks (CNNs) like VGG16 for classifying breast cancer using ultrasound images. We evaluated the models on key performance metrics, including accuracy, precision, recall, and AUC-ROC, while also considering computational factors such as training time and memory consumption. Results show that VGG16 consistently delivers stable performance across different learning rates, while FCNN16 significantly outperforms VGG16 at a lower learning rate of 0.0001, achieving near-perfect classification metrics. These findings highlight the potential of FCNNs for breast cancer diagnosis, though further research is necessary to fully optimize this approach.