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Updated: Jun 29, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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Pixel embedding for grayscale medical image classification.

Wensu Liu^1,2, Na Lv^1,2, Jing Wan^1,2

¹Key Laboratory of Obesity and Glucose/Lipid Associated Metabolic Diseases, China Medical University, Shenyang, Liaoning, 110122, China.

|September 10, 2024

Summary

This summary is machine-generated.

This study introduces a novel method for grayscale medical image classification by combining n-gram features with pixel flattening. The approach effectively preserves spatial information, achieving high accuracy across multiple benchmark datasets.

Keywords:

Classification Grayscale medical image Pixel Text embedding

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Area of Science:

Medical Imaging
Computer Vision
Machine Learning

Background:

Accurate classification of grayscale medical images is crucial for diagnosis.
Existing text embedding architectures require adaptation for image data.
Preserving spatial information is a key challenge in medical image feature extraction.

Purpose of the Study:

To extend text embedding architectures for effective grayscale medical image classification.
To develop a method that preserves spatial information during feature representation.
To evaluate the proposed approach on diverse medical image datasets.

Main Methods:

A novel mechanism combining n-gram features with pixel flattening.
Utilizing column-wise, row-wise, diagonal-wise, and anti-diagonal-wise pixel ordering.
Implementing 10-fold cross-validation for performance assessment.

Main Results:

Achieved 99.92% accuracy on Medical MNIST.
Demonstrated high performance on Chest X-ray Pneumonia (90.06%), Curated Covid CT (96.94%), and Ultrasound (93.17%) datasets.
Obtained 79.11% accuracy on the MIAS dataset, indicating robust performance across varying complexities.

Conclusions:

The proposed method effectively extends text embedding for medical image classification.
The pixel flattening technique successfully preserves crucial spatial information.
The approach shows significant potential for improving diagnostic accuracy in medical imaging.