Optimizing convolutional neural network hyperparameters to enhance liver segmentation accuracy in medical imaging
Abstract
Liver segmentation in medical imaging is a crucial step in various clinical applications, such as disease diagnosis, surgical planning, and evaluation of response to therapy, which require a high degree of precision for accurate results. This research focuses on increasing the accuracy of liver segmentation by optimizing hyperparameters in the convolutional neural network (CNN) model using the developed ResNet architecture. The uniqueness of this research lies in the application of hyperparameter optimization methods such as random search and Bayesian optimization, which allow broader and more efficient exploration than conventional approaches. The results show that the DeepLabV3Plus model (the proposed model) significantly outperforms the standard ResNet in the image segmentation task. DeepLabV3Plus shows excellent performance with an MIoU score of 0.965, a PA Score of 0.929, and a meager loss value of 0.011. These results show that DeepLabV3Plus is able to recognize and predict segmentation areas very accurately and consistently and minimize prediction errors effectively. In conclusion, the results of this study show a significant improvement in segmentation accuracy, with the optimized model providing better performance in the evaluation.
Keywords
Convolutional neural network; DeepLabV3Plus; Hyperparameter optimization; Liver segmentation; ResNet
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PDFDOI: http://doi.org/10.11591/ijece.v15i4.pp3876-3887
Copyright (c) 2025 Iwan Purnama, Agus Perdana Windarto, Solikhun Solikhun
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International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578
This journal is published by theĀ Institute of Advanced Engineering and Science (IAES).