Malaria Parasite Detection in Thick Blood Smears using Deep Learning 
K Venkata Shivaramakrishna Reddy1, S Phani Kumar2
1K Venkata Shiva Rama Krishna Reddy*, Student, Department of Information Technology, Gitam University, Hyderabad (Telangana), India.
2S Phani Kumar, Department of Computer Science, Institution, Gitam University, Hyderabad (Telangana), India.
Manuscript received on December 21, 2021. | Revised Manuscript received on December 26, 2021. | Manuscript published on December 30, 2021. | PP: 86-89 | Volume-11 Issue-2, December 2021. | Retrieval Number: 100.1/ijeat.B32931211221 | DOI: 10.35940/ijeat.B3293.1211221
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Malaria parasitized detection is very important to detect as there are so many deaths due to false detection of malaria in medical reports. So analysis has gained a lot of attention in recent years. Detection of malaria is important as fast as possible because detecting malaria is difficult in blood smears. Our idea is to build a transfer learning model and detect the thick blood smears whether the presence of malaria parasites in a drop of blood. The data consists of 5000 each infected and uninfected data obtained from the NIH website. In this paper, I propose to use three different types of neural networks for the performance evaluation of the malaria data by transfer learning using CNN, VGG19, and fine-tuned VGG19. Transfer learning model performed well among various other models by achieving a precision of 98 percent and an f-1 score of 96 percent.
Keywords: VGG19, Malaria Parasitized, Deep Learning, Convolutional Neural Network, Transfer Learning, Image Augmentation, Python.
Scope of the Article: Deep Learning