Research Article An Ensembled Spatial Enhancement Method for Image Enhancement in Healthcare Muhammad Hameed Siddiqi and Amjad Alsirhani College of Computer and Information Sciences, Jouf University, Sakaka, Aljouf,2014, Saudi Arabia Correspondence should be addressed to Muhammad Hameed Siddiqi; mhsiddiqi@ju.edu.sa Received 15 November 2021; Revised 13 December 2021; Accepted 20 December 2021; Published 4 January 2022 Academic Editor: Liaqat Ali Copyright©2022MuhammadHameedSiddiqiandAmjadAlsirhani.isisanopenaccessarticledistributedundertheCreative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Most medical images are low in contrast because adequate details that may prove vital decisions are not visible to the naked eye. Also,duetothelow-contrastnatureoftheimage,itisnoteasilysegmentedbecausethereisnosignificantchangebetweenthepixel values, which makes the gradient very small Hence, the contour cannot converge on the edges of the object. In this work, we have proposed an ensembled spatial method for image enhancement. In this ensembled approach, we first employed the Laplacian filter, which highlights the areas of fast intensity variation. is filter can determine the sufficient details of an image. e Laplacian filter will also improve those features having shrill disjointedness. en, the gradient of the image has been determined, which utilizes the surrounding pixels for the weighted convolution operation for noise diminishing. However, in the gradient filter, there is one negative integer in the weighting. e intensity value of the middle pixel might be deducted from the surrounding pixels, to enlarge the difference between the head-to-head pixels for calculating the gradients. is is one of the reasons due to which the gradient filter is not entirely optimistic, which may be calculated in eight directions. erefore, the averaging filter has been utilized, which is an effective filter for image enhancement. is approach does not rely on the values that are completely diverse from distinctive values in the surrounding due to which it recollects the details of the image. e proposed approach significantly showed the best performance on various images collected in dynamic environments. 1. Introduction Nowadays, in the real-world society of artificial intelligence (AI), the images might be sensed anytime and anyplace, which are commonly based on the human visualization that intuitively direct the people to easily realize the information that the images carry to us [1]. In healthcare domains, the images comprise various noises, due to which the physicians may face a problem detecting the corresponding diseases. We might utilize the image enhancement technology to diminish the various noises and visual effects to improve the quality of the image [2]. Image enhancement is one of the significant parameters in healthcare domains. Image enhancement is commonly divided into single-point procedures and spatial procedures. e point procedures contain contrast increase, noise re- duction, histogram modulation, and similar colors. Point operations are generally simple nonlinear operations. In contrast, today, linear spatial processes are often used in image processing. e reason is that local linear operations are simple and easy to implement. ough linear image enhancement techniques are frequently suitable in numer- ous applications, important advantages in image enhance- ment might be achieved if nonlinear methods are utilized. e nonlinear methods efficiently reserve the individual characteristics of the image, while the operators using the linear mode distort the image. In addition, nonlinear techniques are less sensitive to noise canceling devices. e noise is always presented because of random physical imagination [3]. ere are various types of image processing and machine learning approaches proposed for the enhancement of im- ages. One approach is to propose and realize the possibility of artificial intelligence and pattern recognition applications which categorize the images through their corresponding pictorial resources such as radiology images. ese days, the Hindawi Journal of Healthcare Engineering Volume 2022, Article ID 9660820, 12 pages https://doi.org/10.1155/2022/9660820