Malaria Detection Using Machine Learning and Image Processing

Malaria is a life-threatening disease caused by Plasmodium parasites and continues to be a significant global health concern, particularly in regions with limited access to healthcare facilities. Prompt and accurate diagnosis of malaria is crucial for effective treatment and control of the disease. So we proposed a novel approach for malaria detection using image processing techniques and machine learning algorithms. Machine learning algorithms are employed to train a classifier using the extracted features. Various supervised learning techniques, such as support vector machines (SVM), random forests, or convolutional neural networks (CNN), are explored for malaria classification. The classifier is trained on a large dataset of label led malaria-positive and malaria-negative images, ensuring the model’s ability to distinguish between infected and uninfected cells accurately.