Leveraging Convolutional Neural Networks for Malaria Detection from Red Blood Cell Images

In recent years, artificial intelligence (AI) has started to be used more and more in the medical field. This paper presents a study focused on malaria classification based on segmented blood cells from images collected from the National Institutes of Health (NIH) database. The research involved the development of a handcrafted convolutional neural network (CNN), as well as experimentation with various fine-tuning approaches using the VGG16 architecture. The conducted experiments have yielded promising results, providing empirical evidence for the potential effectiveness of these techniques in future applications. The augmented CNN achieved an impressive accuracy of 96.51%, while the VGG16 fully trainable model outperformed it with an accuracy of 96.69%. A problem that needs to be analyzed more carefully in the future concerns the explainability of the results so that they can be used with confidence by healthcare professionals.

I. Introduction

Medical imaging is a branch of medicine that uses various imaging technologies to diagnose and treat diseases. These technologies range from X-rays, computed tomography (CT), and magnetic resonance imaging (MRI) to various other advanced methods. Medical image analysis involves extracting useful information from medical images and providing a diagnosis. This may involve tasks such as image segmentation, registration, feature extraction, and classification. Computer-aided diagnosis (CAD) is a subfield of medical image analysis that involves the use of computer algorithms to help doctors make diagnoses [1].