COMPARISON OF DEEP LEARNING ARCHITECTURES FOR ANEMIA CLASSIFICATION USING COMPLETE BLOOD COUNT DATA

Gregorius Airlangga

doi:10.52436/1.jutif.2024.5.3.2109

Gregorius Airlangga Information System Study Program, Engineering Faculty, Universitas Katolik Indonesia Atma Jaya, Indonesia

DOI: https://doi.org/10.52436/1.jutif.2024.5.3.2109

Keywords: Anemia Classification, Complete Blood Count(CBC), Convolutional Neural Network (CNN), Deep Learning, Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN)

Abstract

Anemia is a common condition marked by a deficiency in red blood cells or hemoglobin, affecting the body's ability to deliver oxygen to tissues. Accurate and timely diagnosis is essential for effective treatment. This study aims to classify different types of anemia using complete blood count (CBC) data through the application of deep learning models. We evaluated the performance of four deep learning architectures: Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Fully Connected Network (FCN). The dataset included CBC parameters such as hemoglobin, platelet count, and white blood cell count, labeled with anemia types. Our results indicate that CNN and FCN models achieved the highest test accuracies of 0.85, outperforming MLP and RNN models. This superior performance is due to the ability of CNN and FCN to capture complex patterns and spatial relationships within CBC data. Techniques like data augmentation and weighted loss functions were employed to address class imbalance. These findings demonstrate the potential of deep learning models to automate anemia diagnosis, thereby enhancing clinical decision-making and patient outcomes.

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