Picture segmentation is a crucial task in  computer vision that involves dividing an image into multiple  regions or segments based on various criteria, such as color,  texture, or edge characteristics. Changing artifacts  identification involves identifying and removing artifacts that  are present in the image and affecting the accuracy of segmentation. Artifacts can be caused by various factors, such  as noise, lighting conditions, or image compression.  Identifying and removing these artifacts can improve the  accuracy of segmentation algorithms. Bias modification  involves adjusting the biases present in segmentation  algorithms to improve their accuracy. These biases can lead  to errors in segmentation, such as over-segmentation or  under-segmentation. Bias modification can involve adjusting  the algorithm’s parameters or using different training data to  improve its performance. The use of changing artifacts  identification and bias modification can significantly improve  the accuracy of picture segmentation. By identifying and  removing artifacts and adjusting biases, segmentation  algorithms can more accurately identify and classify objects  in an image, leading to better object detection and  classification. This model is expected to be useful for  damaged images and applications where artifacts and bias are  actual features of interest, such as lesion detection(problem  within medical imaging analysis) and bias field correction in  medical imaging, e.g., Magnetic resonance imaging (MRI).