Enhancing Malaria Detection Through Deep Learning: A Comparative Study of Convolutional Neural Networks
Yassine Benachour, Farid Flitti, Haris M. Khalid
Abstract
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.