MATLAB Code for "Joint Image Processing with Learning-Driven Data Representation and Model Behavior for Non-Intrusive Anemia Diagnosis in Pediatric Patients"

This MATLAB code is part of the study titled "Joint Image Processing with Learning-Driven Data Representation and Model Behavior for Non-Intrusive Anemia Diagnosis in Pediatric Patients", which has been accepted for publication in the Journal of Imaging (MDPI). The code supports image processing, feature extraction, and deep learning model training (including LSTM and RexNet) to classify pediatric patients as anemic or non-anemic based on palm, conjunctival, and fingernail images. Full study details are available in this paper:

Berghout T. Joint Image Processing with Learning-Driven Data Representation and Model Behavior for Non-Intrusive Anemia Diagnosis in Pediatric Patients. Journal of Imaging. 2024; 10(10):245. https://doi.org/10.3390/jimaging10100245 

The datsets use in this work are:

Asare, J. W., Appiahene, P. & Donkoh, E. (2022). Anemia Detection using Palpable Palm Image Datasets from Ghana. Mendeley Data. https://doi.org/10.17632/ccr8cm22vz.1
Asare, J. W., Appiahene, P. & Donkoh, E. (2023). CP-AnemiC (A Conjunctival Pallor) Dataset from Ghana. Mendeley Data. https://doi.org/10.17632/m53vz6b7fx.1
Asare, J. W., Appiahene, P. & Donkoh, E. (2020). Detection of Anemia using Colour of the Fingernails Image Datasets from Ghana. Mendeley Data. https://doi.org/10.17632/2xx4j3kjg2.1