Automated Web-Based Malaria Detection System with Machine Learning and Deep Learning Techniques

Abstract: Malaria parasites pose a significant global health burden, causing widespread suffering and mortality. Detecting malaria infection accurately is crucial for effective treatment and control. However, existing automated detection techniques have shown limitations in terms of accuracy and generalizability. Many studies have focused on specific features without exploring more comprehensive approaches. In our case, we formulate a deep learning technique for malaria-infected cell classification using traditional CNNs and transfer learning models notably VGG19, InceptionV3, and Xception. The models were trained using NIH datasets and tested using different performance metrics such as accuracy, precision, recall, and F1-score. The test results showed that deep CNNs achieved the highest accuracy -- 97%, followed by Xception with an accuracy of 95%. A machine learning model SVM achieved an accuracy of 83%, while an Inception-V3 achieved an accuracy of 94%. Furthermore, the system can be accessed through a web interface, where users can upload blood smear images for malaria detection.