Multiple-Stream Models for a Single-Modality Dataset with Fractal Dimension Features

Multiple-stream deep learning (DL) models are typically used for multiple-modality datasets, with each model extracting favorable features from its own modality dataset. Through feature fusion, multiple-stream models can generally achieve higher recognition rates. While feature engineering is indispensable for machine learning models, it is generally omitted for DL. However, feature engineering can be regarded as an important supplement to DL, especially when using small datasets with rich characteristics. This study aims to utilize limited existing resources to improve the overall performance of the considered models. Therefore, I choose a single-modality dataset—the Chest X-Ray dataset—as my original dataset. For ease of evaluation, I take 16 pre-trained models as basic models for the development of multiple-stream models. Based on the characteristics of the Chest X-Ray dataset, three characteristic datasets are generated from the original dataset, including the Hurst exponent dataset (corresponding to a fractal dimension dataset), as inputs to the multiple-stream models. For comparison, various multiple-stream models are developed based on the same dataset. The experimental results show that, with feature engineering, the accuracy can be raised from 91.67% (one-stream) to 94.52% (two-stream), 94.73% (three-stream), and 94.79% (four-stream), while, without feature engineering, it can be increased from 91.67% to 92.35%, 93.49%, and 93.66%, respectively. In the future, the simple yet effective methodology proposed in this study can be widely applied to other datasets, in order to effectively promote the overall performance of models in scenarios characterized by limited resources.