Affiliation:
1. Department of Electronics and Communication Engineering, Jaypee University of Information Technology, Solan, Himachal Pradesh, India
Abstract
Background:
Diagnosis and treatment planning play a very vital role in improving the
survival of oncological patients. However, there is high variability in the shape, size, and structure
of the tumor, making automatic segmentation difficult. The automatic and accurate detection and
segmentation methods for Brain tumors are proposed in this paper.
Methods:
A modified ResNet50 model was used for tumor detection, and a ResUNetmodel-based
convolutional neural network for segmentation is proposed in this paper. The detection and segmentation were performed on the same dataset consisting of pre-contrast, FLAIR, and postcontrast MRI images of 110 patients collected from the Cancer Imaging Archive. Due to the use
of Residual Networks, the authors observed improvement in evaluation parameters, such as accuracy for tumor detection and dice similarity coefficient for tumor segmentation.
Results:
The accuracy of tumor detection and Dice Similarity Coefficient achieved by the segmentation model were 96.77% and 0.893, respectively, for the TCIA dataset. The results were
compared based on manual segmentation and existing segmentation techniques. The tumor mask
was also individually compared to the ground truth using the SSIM value. The proposed detection
and segmentation models were validated on BraTS2015 and BraTS2017 datasets, and the results
were consensus.
Conclusion:
The use of residual networks in both the detection and the segmentation model resulted in improved accuracy and DSC score. DSC score was increased by 5.9% compared to the
UNet model, and the accuracy of the model was increased from 92% to 96.77% for the test set.
Publisher
Bentham Science Publishers Ltd.
Subject
Drug Discovery,Molecular Medicine,General Medicine
Cited by
1 articles.
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1. Impact of Convolutional Neural Network Hidden Layers on Brain Tumor Detection;2023 Seventh International Conference on Image Information Processing (ICIIP);2023-11-22