Pic2Diagnosis: A Method for Diagnosis of Cardiovascular Diseases from the Printed ECG Pictures

TL;DR

This paper introduces a novel method for diagnosing cardiovascular diseases directly from printed ECG images using a curriculum learning framework and ensemble models, achieving high accuracy without digitization.

Contribution

It presents a new approach that diagnoses CVD directly from ECG images, bypassing digitization, and employs a two-step curriculum learning and ensemble techniques for improved accuracy.

Findings

Achieved an AUC of 0.9534 and F1 score of 0.7801 on the BHF ECG Challenge dataset.

Outperformed individual models with ensemble averaging.

Effectively handles real-world ECG artifacts and simplifies diagnosis process.

Abstract

The electrocardiogram (ECG) is a vital tool for diagnosing heart diseases. However, many disease patterns are derived from outdated datasets and traditional stepwise algorithms with limited accuracy. This study presents a method for direct cardiovascular disease (CVD) diagnosis from ECG images, eliminating the need for digitization. The proposed approach utilizes a two-step curriculum learning framework, beginning with the pre-training of a classification model on segmentation masks, followed by fine-tuning on grayscale, inverted ECG images. Robustness is further enhanced through an ensemble of three models with averaged outputs, achieving an AUC of 0.9534 and an F1 score of 0.7801 on the BHF ECG Challenge dataset, outperforming individual models. By effectively handling real-world artifacts and simplifying the diagnostic process, this method offers a reliable solution for automated CVD diagnosis, particularly in resource-limited settings where printed or scanned ECG images are commonly used. Such an automated procedure enables rapid and accurate diagnosis, which is critical for timely intervention in CVD cases that often demand urgent care.