Effectiveness of Various Smoothing Filters on Malaria Cell Image Quality

Malaria is one of the most vulnerable diseases attacking human health, with rising cases from year to year. Therefore, technological developments are required to overcome this disease. Currently, the examination of malaria is carried out by examining human blood using a microscope, and the results are diagnosed using artificial intelligence (AI) technology. To improve the diagnosis results, this research compared the effectiveness of three image smoothing methods (average, Gaussian, and median filters) in reducing Gaussian and Salt and Pepper noise, commonly present in malaria cell images. These filters were tested on four different noise levels and analyzed based on PSNR, SNR, MSE, and SSIM values to determine the most effective filter. The results unveiled that the average filter depicted the most remarkable performance on Gaussian noise, while the median filter produced the highest outcome on Salt and Pepper noise.