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Transfer Learning and Handcrafted Features Ensembles for Ultrasound Breast Cancer Image Classification

Year: 2025

Type: article

Abstract: Breast cancer is the most commonly diagnosed cancer in women. Its diagnosis via ultrasound imaging largely depends on the technical skill of the radiologist. This study developed a binary classification system for breast lesions, combining transfer learning models and handcrafted features in ultrasound images. Pre-trained CNNs like InceptionV3, EfficientNetB4, ResNet50, and VGG16 were used, along with SVM-classified handcrafted features. Models were individually analyzed and combined using late-fusion ensembles. ResNet50 achieved an F1-score of 81.97%. The best late-fusion ensemble model reached an F1-score of 83.90%. In the cross-dataset evaluation, the top late-fusion ensemble model in the development dataset scored an F1-score of 88.70% and 78.20% in the test BUSI and BUID datasets, respectively. These results emphasize the robust potential of a late-fusion ensemble that combines CNN transfer learning and handcrafted features to classify breast lesions in ultrasound images.

Source: Revista de Informática Teórica e Aplicada

DOI: 10.22456/2175-2745.143357