A Modified Random Walk with Improved Weight–Weighted Sampling (MRWIW–WS) is a novel image Random Walk Algorithm (RWA) and Modified Random Walk with Improved Weight Algorithm (MRWIW–A)-based segmentation technique in which the accuracy of lung tumor detection is increased by RWIW–WS. The DSC are used in this paper and their effect on segmenting the objects that contain the different geometric feature, i.e., the lung tumors for three segmented objects are same. RWA, MRWIWA, and RWIW–WS were used to find out if the masks of these objects. DSC were utilized to calculate/estimate the segmentation results, in case of RWA, MRWIWA, and RWIW–WS an indicated average DSC 0.92, 0.94 were obtained. Furthermore, the algorithm measured the tumor’s area, main axis, minor axis, and perimeter with accuracy. It does, however, necessitate greater processing time and computational power. By offering a reliable segmentation technique for lung tumor identification, this work advances the field of medical image analysis.