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2025;21 (1): 15-27.

Detection of Brain Tumor Employing Residual Network-based Optimized Deep Learning.

Saransh Rohilla, Shruti Jain
Author Information
  1. Saransh Rohilla: Department of Electronics and Communication Engineering, Jaypee University of Information Technology, Solan, Himachal Pradesh, India. ORCID
  2. Shruti Jain: Department of Electronics and Communication Engineering, Jaypee University of Information Technology, Solan, Himachal Pradesh, India. ORCID
PMID: 37587819 DOI: 10.2174/1573409920666230816090626

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.

Keywords

Brain tumor segmentation MRI ResNet50 ResUNet convolutional neural network deep learning.

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MeSH Term

Humans
Deep Learning
Brain Neoplasms
Magnetic Resonance Imaging
Neural Networks, Computer
Image Processing, Computer-Assisted
Journal Article

Word Cloud

Created with Highcharts 10.0.0segmentationtumordetectionmodelaccuracyproposedcomparedpatientsautomaticpaperResNet50convolutionalneuralnetworkdatasetMRIuseresidualnetworksdicesimilaritycoefficient9677%resultsDSCscoreincreasedBrainBACKGROUND:DiagnosistreatmentplanningplayvitalroleimprovingsurvivaloncologicalHoweverhighvariabilityshapesizestructuremakingdifficultaccuratemethodsbraintumorsMETHODS:modifiedusedResUNetmodel-basedperformedconsistingpre-contrastFLAIRpostcontrastimages110collectedcancerimagingarchiveDueauthorsobservedimprovementevaluationparametersRESULTS:achieved0893respectivelyTCIAbasedmanualexistingtechniquesmaskalsoindividuallygroundtruthusingSSIMvaluemodelsvalidatedBraTS2015BraTS2017datasetsconsensusCONCLUSION:resultedimproved59%UNet92%testsetDetectionTumorEmployingResidualNetwork-basedOptimizedDeepLearningResUNetdeeplearning

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