Enhancing Malaria Detection Through Deep Learning: A Comparative Study of Convolutional Neural Networks

The need for accurate and efficient malaria diagnosis has driven research into automated solutions using deep learning. This study presents a comparative analysis of five convolutional neural network (CNN) models for malaria detection in microscopic blood cell images. We evaluated models ranging from a basic architecture to advanced iterations employing data augmentation and VGG16 transfer learning. Using a dataset of 24,958 training and 2,600 test images, we assessed performance based on accuracy, precision, recall, F1-score, and computational efficiency. Our results demonstrate that a simpler CNN model (Model_1) achieved greater than 99% accuracy, surpassing the performance of more complex models while requiring significantly less computational resources. This finding underscores the potential of efficient deep learning strategies for developing scalable and cost-effective malaria diagnostic tools.