Improving Brain Tumor Image Segmentation Accuracy Based on Residual Network (ResNet) Using Nearest Neighbor Upsampling - Muhammad Afifudin; Endang Sugiharti (Universitas Negeri Semarang, 2026)
Abstract. Brain tumors are a critical disease due to the abnormal growth of cells in the brain, which can damage surrounding normal cells and increasing the risk of death in patients. With advancements in technology, artificial intelligence can be developed to segment brain tumor areas, aiding medical professionals in identifying tumor characteristics and determining appropriate treatment plans. Convolutional Neural Network (CNN) models can be utilized for segmentation tasks because their ability to classify each pixel of an image, assign specific labels, and map them into homogeneous groups. To enhance the capability of CNNs against the possibility of vanishing gradients, the Residual Network (ResNet) architecture can be applied to the segmentation model. The use of ResNet provides additional capability for the network to choose between the training results in the current epoch or skip to the next network when the training results approach the identity value. However, ResNet also reduces the scale of images and feature maps during downsampling operations, sacrificing spatial resolution. This study proposes the implementation of the Nearest Neighbor Upsampling method on ResNet to improve the model's accuracy in the task of MRI brain tumor segmentation. Purpose: This research proposes a method to increase the accuracy of brain tumor MRI image segmentation using the ResNet model by implementing the Nearest Neighbor Upsampling method. Methods/Study design/approach: The method used is Nearest Neighbor Upsampling on ResNet to enhance image dimensions and fill gaps in MRI brain tumor images during the learning process, preserving spatial information and context crucial for segmentation. Result/Findings: The optimization of the brain segmentation model for classifying brain tumor regions using ResNet and Nearest Neighbor Upsampling achieves an increase in accuracy from 96.94% to 98.44% and a decrease in loss value from 0.0881 to 0.0874. Novelty/Originality/Value: This research addresses the limitations of the Residual Network (ResNet) model, such as the drawback of reducing the scale of images and feature maps during downsampling, resulting in a loss of spatial resolution. To overcome this challenge, the study introduces the Nearest Neighbor Upsampling method applied to ResNet, demonstrating its effectiveness in representing spatial information and image context, thereby improving segmentation accuracy.