Detection of sickle cell disease using deep neural networks and explainable artificial intelligence

Abstract

Sickle cell disease (SCD), a blood disorder that transforms the shape of red blood cells into a distinctive sickle form, is a major concern as it not only compromises the blood’s oxygen-carrying capacity but also poses significant health risks, ranging from weakness to paralysis and, in severe cases, even fatality. This condition not only underscores the pressing need for innovative solutions but also encapsulates the broader challenges faced by medical professionals, including delayed treatment, protracted processes, and the potential for subjective errors in diagnosis and classification. Consequently, the application of artificial intelligence (AI) in healthcare has emerged as a transformative force, inspiring multidisciplinary efforts to overcome the complexities associated with SCD and enhance diagnostic accuracy and treatment outcomes. The use of transfer learning helps to extract features from the input dataset and give an accurate prediction. We analyse and compare the performance parameters of three distinct models for this purpose: GoogLeNet, ResNet18, and ResNet50. The best results were shown by the ResNet50 model, with an accuracy of 94.90%. Explainable AI is the best approach for transparency and confirmation of the predictions made by the classifiers. This research utilizes Grad-CAM to interpret and make the models more reliable. Therefore, this specific approach benefits pathologists through its speed, precision, and accuracy of classification of sickle cells.

Keywords

• deep learning
• explainable artificial intelligence
• grad-cam
• sickle cell disease