Comparing Transfer Learning to Feature Optimization in Microstructure Classification

Human analysis of research data is slow and inefficient. In recent years machine learning tools have advanced our capability to perform tasks normally carried out by humans, such as image segmentation and classification. In this work, we seek to further improve binary classification models for high throughput identification of different microstructural morphologies. We utilize a dataset with limited observations (133 dendritic structures, 444 non-dendritic) and employ data augmentation via rotation and translation to enhance the dataset six-fold. Then, transfer learning is carried out using pre-trained networks VGG16, InceptionV3, and Xception achieving only moderate F1 scores (0.801 to 0.822). We hypothesize that feature engineering could yield better results than transfer learning alone. To test this, we employ a new nature-inspired feature optimization algorithm, the Binary Red Deer Algorithm (BRDA), to carry out binary classification and observe F1 scores above 0.93.