PREDICTION OF MALARIAL INFECTION USING DEEP LEARNING

Computer Engineering

One of the most lethal and deadly diseases in the entire planet is malaria. Because of the plasmodium parasite, the mosquito is the main source of the disease. If it is not recognized and treated at an early stage, it could get worse and result in death. We have established procedures for diagnosing malaria, which involve experts looking at the blood cells under a microscope. One of the most popular methods for diagnosing malaria by a skilled microscopist is the rapid diagnosis test, however it takes a long time to do and can produce inaccurate results if there is a flaw or human error.We require a detecting method to raise awareness of these diseases in order to solve this issue. A component of artificial intelligence is deep learning. It includes a wide variety of algorithms that can be used to forecast any given natural event. The medical field heavily relies on artificial intelligence. Here, the picture preprocessing and feature extraction were done using a convolutional neural network. Building a model using CNN will require a huge amount of data so we have used the Transfer Learning-VGG model along with the CNN to reduce time and for better accuracy. In this paper, we are going to detect whether the patient has malaria or not by using deep learning and transfer learning techniques, then integrating it with the web application with the help of python libraries.

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