Breast Cancer Image Classification Using Phase Features and Deep Ensemble Models - Edgar Omar Molina Molina; Victor H. Diaz-Ramirez (Applied Sciences)
Breast cancer is a leading cause of mortality among women worldwide. Early detection is crucial for increasing patient survival rates. Artificial intelligence, particularly convolutional neural networks (CNNs), has enabled the development of effective diagnostic systems by digitally processing mammograms. CNNs have been widely used for the classification of breast cancer in images, obtaining accurate results similar in many cases to those of medical specialists. This work presents a hybrid feature extraction approach for breast cancer detection that employs variants of EfficientNetV2 network and convenient image representation based on phase features. First, a region of interest (ROI) is extracted from the mammogram. Next, a three-channel image is created using the local phase, amplitude, and orientation features of the ROI. A feature vector is constructed for the processed mammogram using the developed CNN model. The size of the feature vector is reduced using simple statistics, achieving a redundancy suppression of <semantics>99.65%</semantics>. The reduced feature vector is classified as either malignant or benign using a classifier ensemble. Experimental results using a training/testing ratio of 70/30 on 15,506 mammography images from three datasets produced an accuracy of <semantics>86.28%</semantics>, a precision of <semantics>78.75%</semantics>, a recall of <semantics>86.14%</semantics>, and an F1-score of <semantics>80.09%</semantics> with the modified EfficientNetV2 model and stacking classifier. However, an accuracy of <semantics>93.47%</semantics>, a precision of <semantics>87.61%</semantics>, a recall of <semantics>93.19%</semantics>, and an F1-score of <semantics>90.32%</semantics> were obtained using only CSAW-M dataset images.