Utilising Transfer Learning for the Identification of Malarial Parasite

Today, Anopheles mosquito bites and Plasmodium parasites are the primary means of transmission for the deadly life-threatening disease known as malaria. Spread-out blood smears must be inspected under a microscope for this technique by qualified professionals or competent staff. The creation of smart computers that replicate the behaviour in humans has been significantly influenced by deep learning models. Computer vision research, especially in the field of health, has employed transfer learning. Microscopic images are frequently used as input images in the medical field. These systems address problems in all relevant sectors utilising Deep Neural Network architecture, which is adept at applying analytical and logical reasoning. In the ever-evolving field of digital image processing, deep learning has presented us with challenges. In this study, we examine several deep learning methods for malaria diagnosis. Convolutional neural networks are employed in the first categorization of the cell images.