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 space pose significant computational challenges%2C primarily stemming from limited data availability. In response%2C this study introduces a novel data augmentation technique%2C aimed at automating spine tumor segmentation and localization through AI approaches. Leveraging a fusion of fuzzy c-means clustering and Random Forest algorithms%2C the proposed method achieves successful spine tumor segmentation based on predefined masks initially delineated by domain experts in medical imaging. Subsequently%2C a Convolutional Neural Network (CNN) architecture is employed for tumor classification. Moreover%2C 3D vertebral segmentation and labeling techniques are used to help pinpoint the exact location of the tumors in the lumbar spine. Results indicate a remarkable performance%2C with 99%25 accuracy for tumor segmentation%2C 98%25 accuracy for tumor classification%2C and 99%25 accuracy for tumor localization achieved with the proposed approach. These metrics surpass the efficacy of existing state-of-the-art techniques%2C as evidenced by superior Dice Score%2C Class Accuracy%2C and Intersection over Union (IOU) on class accuracy metrics. This innovative methodology holds promise for enhancing the diagnostic capabilities in detecting and characterizing spinal tumors%2C thereby facilitating more effective clinical decision-making.?quality=80&w=800)
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