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@article{201608,
        author = {Devesh Sinsinwar and Khushiram Sharma and Ayush Kumar and Ulfat Ara},
        title = {AUTOMATED DETECTION OF MALARIA PARASITE IN BLOOD SMEAR IMAGE},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {4522-4537},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=201608},
        abstract = {Malaria is a serious disease that has caused millions of deaths around the world. According to the World Health Organization (WHO), about 610,000 people died from malaria in 2024.  Malaria spreads to humans through the bite of infected mosquitoes that carry Plasmodium parasites. The disease causes high fever with shivering, chills, and headaches, which greatly weaken the body. Children and elderly people are at a higher risk of death from malaria. One major reason for the high death rate is that the parasites in the blood can usually be detected only about 7 days after a mosquito bite. Treatment with antimalarial medicines can begin only after confirming the presence of the parasite. Sometimes, this delay in treatment can become life-threatening. To overcome these limitations, the development of automated diagnostic systems has become essential for the rapid and reliable detection of malaria. Such systems can help reduce the false negative rate and support early diagnosis, which is critical for effective treatment and disease management. Traditionally, malaria detection was done using a blood smear test. In this method, a small blood sample is placed on a glass slide and examined under a microscope to detect malaria parasites. This method requires skilled laboratory technicians and careful observation. Nowadays, computer-aided techniques are used to detect malaria parasites in blood smear images. These systems analyze microscopic images using image processing and machine learning methods. Computer-assisted techniques help reduce human errors and improve the accuracy of diagnosis.
Malaria can also be detected using the rapid card method, commonly known as the Rapid Diagnostic Test (RDT). In this method, a small drop of blood is placed on a test card, and the result appears within a few minutes. It is a quick and simple method that does not require a microscope. The proposed study has been carried out to identify malaria parasites in microscopic blood smear images using computer-assisted techniques to improve early and accurate diagnosis.
To evaluate the performance of the proposed algorithm, experiments were conducted using the National Institutes of Health (NIH) Malaria dataset, and the results were compared with those of existing methods. The proposed approach achieved a detection accuracy of more than 91%, demonstrating superior performance over comparable techniques. These findings indicate that the developed algorithm is reliable and can serve as a valuable support tool for pathologists and haematologists in the accurate detection of malaria parasites.},
        keywords = {},
        month = {May},
        }