  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.
