Pixel embedding for grayscale medical image classification
Wensu Liu,
Na Lv,
Jing Wan,
Lu Wang,
Xiaobei Zhou
Affiliations
Wensu Liu
Key Laboratory of Obesity and Glucose/Lipid Associated Metabolic Diseases, China Medical University, Shenyang, Liaoning, 110122, China; Institute of Health Sciences, China Medical University, Shenyang, Liaoning, 110122, China
Na Lv
Key Laboratory of Obesity and Glucose/Lipid Associated Metabolic Diseases, China Medical University, Shenyang, Liaoning, 110122, China; Institute of Health Sciences, China Medical University, Shenyang, Liaoning, 110122, China
Jing Wan
Key Laboratory of Obesity and Glucose/Lipid Associated Metabolic Diseases, China Medical University, Shenyang, Liaoning, 110122, China; Institute of Health Sciences, China Medical University, Shenyang, Liaoning, 110122, China
Lu Wang
Library of Shengjing Hospital of China Medical University, Shenyang, Liaoning, 110122, China; School of Health Management, China Medical University, Shenyang, Liaoning, 110122, China; Corresponding author.
Xiaobei Zhou
Key Laboratory of Obesity and Glucose/Lipid Associated Metabolic Diseases, China Medical University, Shenyang, Liaoning, 110122, China; Institute of Health Sciences, China Medical University, Shenyang, Liaoning, 110122, China; Corresponding author.
In our paper, we present an extension of text embedding architectures for grayscale medical image classification. We introduce a mechanism that combines n-gram features with an efficient pixel flattening technique to preserve spatial information during feature representation generation. Our approach involves flattening all pixels in grayscale medical images using a combination of column-wise, row-wise, diagonal-wise, and anti-diagonal-wise orders. This ensures that spatial dependencies are captured effectively in the feature representations. To evaluate the effectiveness of our method, we conducted a benchmark using 5 grayscale medical image datasets of varying sizes and complexities. 10-fold cross-validation showed that our approach achieved test accuracy score of 99.92 % on the Medical MNIST dataset, 90.06 % on the Chest X-ray Pneumonia dataset, 96.94 % on the Curated Covid CT dataset, 79.11 % on the MIAS dataset and 93.17 % on the Ultrasound dataset. The framework and reproducible code can be found on GitHub at https://github.com/xizhou/pixel_embedding.
