Enhancing Cancer Diagnosis
A Hybrid Level-Set and Edge Detection Approach for Accurate Medical Image Segmentation
Abstract
Early diagnosis of cancer is crucial for improved patient results. With the aim of improving the effectiveness of cancer diagnosis, this paper introduces a new proposed method, computer-aided diagnosis, utilizing the level-set algorithm based on the edge detection approach for medical image segmentation. To assess the performance of our method, it was proven on a highly varied dataset that comprised liver cancer, Magnetic Resonance Imaging (MRI) brain cancer, and dermoscopy color images. By effectively integrating edge information into the level-set evolution process, the proposed method achieved impressive results. For liver cancer images, we obtained an accuracy of 0.9913, a sensitivity of 0.9165, and a Dice coefficient of 0.8820. Similarly, for dermoscopy color images, the method achieved an accuracy of 0.9979, a sensitivity of 0.9301, and a Dice coefficient of 0.9301. In the case of MRI images, the method demonstrated an accuracy of 0.9933, a sensitivity of 0.8591, and a Dice coefficient of 0.8591. The proposed method outperforms traditional techniques such as Simulated Annealing combined with Artificial Neural Network and Fuzzy Entropy with Level Set thresholding. This method demonstrates superior segmentation accuracy and robustness. By enabling precise identification of cancerous regions, this approach supports early diagnosis, reduces misdiagnosis, and enhances treatment planning, offering significant potential for improving cancer care and patient results.
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References
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