Automated RBC Morphology Counting and Grading Using Image Processing and Support Vector Machine

Pellegrino, Rosemarie V.; Tarrobago, Aubrey C.; Zulueta, Dave Lester B.

doi:10.1109/hnicem54116.2021.9731937

Cited by 13 publications

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Dynamic modeling of photoacoustic sensor data to classify human blood samples

Pérez-Pacheco,

Ramírez-Chavarría,

Quispe-Siccha

et al. 2023

Med Biol Eng Comput

View full text Add to dashboard Cite

The photoacoustic effect is an attractive tool for diagnosis in several biomedical applications. Analyzing photoacoustic signals, however, is challenging to provide qualitative results in an automated way. In this work, we introduce a dynamic modeling scheme of photoacoustic sensor data to classify blood samples according to their physiological status. Thirty-five whole human blood samples were studied with a state-space model estimated by a subspace method. Furthermore, the samples are classified using the model parameters and the linear discriminant analysis algorithm. The classification performance is compared with time- and frequency-domain features and an autoregressive-moving-average model. As a result, the proposed analysis can predict five blood classes: healthy women and men, microcytic and macrocytic anemia, and leukemia. Our findings indicate that the proposed method outperforms conventional signal processing techniques to analyze photoacoustic data for medical diagnosis. Hence, the method is a promising tool in point-of-care devices to detect hematological diseases in clinical scenarios. Graphical abstract

show abstract