EigenHearts: Cardiac Diseases Classification Using EigenFaces Approach (2411.16227v1)

Abstract: In the realm of cardiovascular medicine, medical imaging plays a crucial role in accurately classifying cardiac diseases and making precise diagnoses. However, the field faces significant challenges when integrating data science techniques, as a significant volume of images is required for these techniques. As a consequence, it is necessary to investigate different avenues to overcome this challenge. In this contribution, we offer an innovative tool to conquer this limitation. In particular, we delve into the application of a well recognized method known as the EigenFaces approach to classify cardiac diseases. This approach was originally motivated for efficiently representing pictures of faces using principal component analysis, which provides a set of eigenvectors (aka eigenfaces), explaining the variation between face images. As this approach proven to be efficient for face recognition, it motivated us to explore its efficiency on more complicated data bases. In particular, we integrate this approach, with convolutional neural networks (CNNs) to classify echocardiography images taken from mice in five distinct cardiac conditions (healthy, diabetic cardiomyopathy, myocardial infarction, obesity and TAC hypertension). Performing a preprocessing step inspired from the eigenfaces approach on the echocardiography datasets, yields sets of pod modes, which we will call eigenhearts. To demonstrate the proposed approach, we compare two testcases: (i) supplying the CNN with the original images directly, (ii) supplying the CNN with images projected into the obtained pod modes. The results show a substantial and noteworthy enhancement when employing SVD for pre-processing, with classification accuracy increasing by approximately 50%.

• The paper introduces a novel EigenHearts method that adapts the EigenFaces technique using SVD to effectively preprocess and classify cardiac echocardiograms.
• It demonstrates that SVD preprocessing boosts CNN accuracy from 81% to 97% for LAX views and from 76% to 93% for SAX views.
• The study offers promising insights for improved diagnostic methods and encourages further integration of classical techniques with advanced ML models in biomedical imaging.

Overview of "EigenHearts: Cardiac Diseases Classification Using EigenFaces Approach"

The paper "EigenHearts: Cardiac Diseases Classification Using EigenFaces Approach" presents an innovative methodology for classifying cardiac diseases by applying the EigenFaces approach, originally developed for facial recognition, to cardiac imaging data. This research primarily aims to address the challenges that arise due to the stringent data requirements in medical imaging, focusing on cardiac echocardiography images in this case.

Methodological Insights

This paper capitalizes on the Singular Value Decomposition (SVD) and Principal Component Analysis (PCA) for preprocessing echocardiography images derived from mouse models under various cardiac conditions: healthy, diabetic cardiomyopathy, myocardial infarction, obesity, and transverse aortic constriction (TAC) hypertension. The key innovation lies in the adaptation of the EigenFaces technique—herein referred to as "EigenHearts"—to succinctly represent and classify the cardiac conditions by capturing the principal modes of variation within and across these medical images.

The procedure involves generating mean-subtracted image matrices for each cardiac condition, applying SVD to these matrices, and creating a basis of "eigenhearts" analogous to eigenfaces. These eigenmodes serve as a compact feature representation that feeds into a convolutional neural network (CNN) for classification tasks. Two types of datasets, representing long and short-axis views (LAX and SAX) of the echocardiogram, are evaluated to test the efficacy of this preprocessing strategy.

Key Results

Quantitatively, the research demonstrates that employing SVD as a preprocessing step significantly enhances classification performance. When using the CNN on LAX datasets, classification accuracy sees an improvement from a modest 81% with original images to 97% with images processed through the EigenHearts technique. Similarly, classification accuracy for SAX datasets improves from 76% to 93% upon application of SVD preprocessing. This represents a substantial increase in classification power, reinforcing the benefits of dimension reduction and noise filtering inherent in the SVD methodology.

Moreover, the paper showcases the robustness of the EigenHearts approach, as evidenced by high validation accuracy across varied truncation levels of the SVD, thus underscoring its capacity for facilitating accurate predictions even with complex image sets.

Implications and Future Directions

The implications of this research extend into practical enhancements in medical diagnostics, especially in tailored applications where large datasets are scarce and where improved feature extraction methodologies could lead to better disease prediction models. The adoption of matrix decomposition techniques, typically established in fields like signal processing and facial recognition, marks a cross-pollination of methodologies with potentially transformative impacts in biomedical engineering.

In theoretical terms, this paper illustrates the potential of integrating classical mathematical techniques like SVD with contemporary deep learning frameworks to innovate solutions that surpass traditional computer vision approaches.

For future research, further exploration into the integration of more sophisticated machine learning models with this eigenbasis approach could yield even higher accuracy and generalizability. Expanding this approach to encompass various imaging modalities and augmenting datasets through advanced synthetic generation techniques such as GANs could also reinforce its utility across broader biomedical applications.

This paper exemplifies the confluence of classical techniques and modern computational approaches in addressing specific challenges within the medical imaging domain, setting the stage for new investigations that could refine diagnostic methodologies and potentially lead to more personalized healthcare outcomes.