Eigenhearts: Cardiac diseases classification using eigenfaces approach.
Authors
Affiliations (5)
Affiliations (5)
- ETSI Aeronáutica y del Espacio - Universidad Politécnica de Madrid, Pl. del Cardenal Cisneros, 3, 28040, Madrid, Spain; Université Mohamed Khider Biskra, BP 145 RP, 07000, Biskra, Algeria. Electronic address: [email protected].
- Departamento de Medicina y Cirugía Animal, Facultad de Veterinaria - Universidad Complutense de Madrid, Av. Puerta de Hierro, 28040, Madrid, Spain; Centro Nacional de Investigaciones Cardiovasculares (CNIC), C. de Melchor Fernández Almagro, 3, 28029, Madrid, Spain.
- Departamento de Medicina y Cirugía Animal, Facultad de Veterinaria - Universidad Complutense de Madrid, Av. Puerta de Hierro, 28040, Madrid, Spain.
- Centro Nacional de Investigaciones Cardiovasculares (CNIC), C. de Melchor Fernández Almagro, 3, 28029, Madrid, Spain.
- ETSI Aeronáutica y del Espacio - Universidad Politécnica de Madrid, Pl. del Cardenal Cisneros, 3, 28040, Madrid, Spain; Center for Computational Simulation (CCS), Boadilla del Monte, 28660, Madrid, Spain.
Abstract
In the realm of cardiovascular medicine, medical imaging plays a crucial role in accurately classifying cardiac diseases and making precise diagnoses. However, the integration of data science techniques in this field presents significant challenges, as it requires a large volume of images, while ethical constraints, high costs, and variability in imaging protocols limit data acquisition. 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. Given its effectiveness in face recognition, we sought to evaluate its applicability to more complex medical imaging datasets. In particular, we integrate this approach with convolutional neural networks to classify echocardiography images taken from mice in five distinct cardiac conditions (healthy, diabetic cardiomyopathy, myocardial infarction, obesity and TAC hypertension). The results show a substantial and noteworthy enhancement when employing the singular value decomposition for pre-processing, with classification accuracy increasing by approximately 50%.