Deep Learning Advancements in Malaria Diagnosis

Abstract

The authors introduce a robust convolutional neural network (CNN) model for malaria-infected cell identification, achieving over 96.5% test accuracy using PyTorch and GPU acceleration. Data augmentation ensures dataset suitability, while this MosquitoNet CNN architecture effectively extracts hierarchical features through three convolutional and fully linked layers. Training over 20 epochs with cross-entropy loss and Adam optimizer yields high accuracy on independent testing subsets, supported by detailed class-wise metrics and a confusion matrix visualization. This approach integrates deep learning, data augmentation, and advanced visualization for comprehensive malaria detection, promising significant advancements in medical diagnostics. Future work may explore hyperparameter tuning and transfer learning for further enhancement. This research contributes to the field with its robust methodology and high accuracy, offering a promising tool for malaria diagnosis and beyond.