MalaNet: A Small World Inspired Neural Network for Automated Malaria Diagnosis

In this work, a novel neural network architecture called MalaNet is proposed for the detection and diagnosis of malaria, an infectious disease that poses a major global health challenge. The proposed neural network architecture is inspired by small-world network principles, which generally involve the introduction of new links. A small-world neural network is realized by establishing new connections, thereby reducing the average path length and increasing clustering coefficient. These characteristics are known to enhance interconnectivity and improve feature propagation within the network. In the context of malaria diagnosis, these characteristics of MalaNet can enhance detection accuracy and enable better generalization in scenarios with limited data availability. Broadly, two variants of MalaNet are proposed in this work. First, a small-world-inspired Feed-Forward Neural Network (FNN) is developed for symptom and categorical feature-based diagnosis, providing an accessible solution when blood smear images are unavailable. Subsequently, a small-world-inspired Convolutional Neural Network (CNN) is developed for precise and automated diagnosis when blood smear images are available. Both variants of MalaNet are rigorously validated using the National Institute of Health Malaria dataset, a clinical dataset from Federal Polytechnic Ilaro Medical Centre, Nigeria, and the APTOS dataset. Comparative results against several state-of-the-art neural network models in the literature demonstrate MalaNet’s superior performance, generalization capability, and computational efficiency. The small-world neural network architecture proposed in this work enhances feature learning, diagnostic accuracy, and adaptability in limited-data and resource-constrained settings, motivating its application in disease diagnosis where timely and accurate results are critical.