<p>Medical image analysis has emerged as a potential solution for the study, early diagnosis and prediction of various diseases. For this purpose, it is pertinent that the images are noise-free. This is a challenge since medical images have inherent noise due to the bias introduced while image capturing by by different imaging modalities. It is of utmost importance to denoise medical images for better image analysis and performance. This paper presents an enhanced iterative mean filter method called EIMF for denoising medical computed tomography (CT) scan images by integrating a noise detector along with a modified version of iterative mean filter. To evaluate the performance of our method, it has been tested on 380 medical CT images and 73 standard images and compared with nine other state-of-the-art methods namely AFMF, AWMF, DAMF, IMF, NAFSM, PSMF, ACmF, ASWMF and DAMRmF, using three popular noise assessment metrics such as PSNR, MSSIM and MAE, and distance metric and has been found to give significantly better results. A recent comparative analysis on three public benchmark datasets (Set12, Bsd68 and Urban100) with 12 advanced methods namely Bm3d, Wnnm, DnCnn, Ircnn, FfdNet, N3Net, Nlrn, FocNet, MwCnn, DruNet, SwinIr and restormer further confirms EIMF’s effectiveness under various noise conditions. An enhanced iterative mean filter called EIMF has been introduced for effectively denoising various medical CT images. We found that our method outperforms nine state-of-the-art methods with respect to four evaluation parameters for all low, medium and high density noises. However, there is a little scope of improvement for very high density (90%) noise. In future, we can test the efficiency of our method for other medical imaging modalities.</p>