Digital holographic technique based breast cancer detection using transfer learning method

The digital holographic technique is an interferometric method that provides comprehensive information on morphological traits such as cell layer thickness and shape as well as access to biophysical attributes of cells like refractive index, dry mass, and volume. This method helps characterize sample structures in three dimensions both statically and dynamically, even for transparent objects like living biological cells. This research work captures the digital holograms of breast tissues and analyzes the malignancy of the tissue using a deep learning technique. It enables dynamic measurement of the sample under investigation. Different transfer learning models such as Inception, DenseNet, SqueezeNet, VGG, and ResNet are incorporated in this work. The parameters accuracy, precision, sensitivity, and F1 score of different models are compared and found that the ResNet model outperforms better compared to other models.