Osako, DaisukeDaisukeOsakoJIAN-JIUN DING2025-10-272025-10-272025-08-29https://scholars.lib.ntu.edu.tw/handle/123456789/733116Accurate tumor segmentation is crucial for cancer diagnosis and treatment planning. We developed a hybrid framework combining complementary convolutional neural network (CNN) models and advanced post-processing techniques for robust segmentation. Model 1 uses contrast-limited adaptive histogram equalization preprocessing, CNN predictions, and active contour refinement, but struggles with complex tumor boundaries. Model 2 applies noise-augmented preprocessing and iterative detection to enhance the segmentation of subtle and irregular regions. The outputs of both models are merged and refined with edge correction, size filtering, and a spatial intensity metric (SIM) expansion to improve under-segmented areas, an approach that achieves higher F1 scores and intersection over union scores. The developed framework highlights the potential in combining machine learning and image-processing techniques to develop reliable diagnostic tools.[SDGs]SDG3conference paper10.3390/engproc2025108006