Malaria Detection Using Machine Learning

Malaria, a significant global health concern, necessitates precise diagnostic tools for effective management. This study introduces an innovative approach to malaria detection using advanced machine-learning techniques. By harnessing convolutional neural networks (CNNs) and deep learning, the research presents a robust framework for automated malaria detection through microscopic images of red blood cells. The study evaluates three key algorithms-CNN, VGG-16, and Support Vector Machine (SVM)-using a meticulously curated dataset of 27,560 images. Results highlight the VGG-16 model's exceptional accuracy, achieving 98.5%. Transfer learning is pivotal in its success, demonstrating the power of pre-trained models for medical image analysis. This research advances automated disease diagnosis, particularly in resource-limited settings. Future work involves fine-tuning algorithms, exploring ensemble techniques, and enhancing interpretability for broader healthcare applications.