Image Segmentation in Multimodal Medical Imaging Using Deep Learning Models

Pradeep Kumar Tripathi (), Sarvachan Verma, Birendra Kumar (), Achintya Kumar Pandey (), Pankaj Singh (), Jagendra Singh () and Jyotsna Ghildiyal Bijawan ()
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Pradeep Kumar Tripathi: Ajay Kumar Garg Engineering College
Sarvachan Verma: Ajay Kumar Garg Engineering College
Birendra Kumar: Ajay Kumar Garg Engineering College
Achintya Kumar Pandey: Ajay Kumar Garg Engineering College
Pankaj Singh: Ajay Kumar Garg Engineering College
Jagendra Singh: Bennett University
Jyotsna Ghildiyal Bijawan: British University

A chapter in Machine Learning and Deep Learning Modeling and Algorithms with Applications in Medical and Health Care, 2025, pp 391-412 from Springer

Abstract: Abstract Diagnosis and staging of brain cancer are critical for on-time and appropriate treatment. Generally, manual interpretation of this image data is very time-consuming and subjective. Therefore, the current research aims to find an effective and accurate automated computer-assisted classification of brain tumours using multimodal imaging data, such as CT, MRI, and PET scans. We develop and tune deep learning models, VGG16, VGG19, U-Net, and Gated Recurrent Unit (GRU), to lift the reliability and precision of the analyses of tumor segmentation. The dataset is pre-processed to extract significant features and biomarkers such that spatial and temporal information for each modality is captured. 70% of the data is used for the training of models, and 30% of the data is kept for testing. The highest prediction accuracy was seen in GRU, giving 98.45% in the prediction of brain cancer and its stage, higher than other models. VGG19 followed at a rate of 94.56%. U-Net reached 89.34%, and VGG16 84.5%. Thus, it is beneficial to employ these tests in a clinical, real-time setting, as correct staging and classification frequently improve patient outcomes. Such findings demonstrate that the integration of multi-streamed data into high-order DL models can achieve superior performance in diagnosis and serves as an initial step toward the completely automated investigation of the brain tumor.

Keywords: Brain tumor segmentation; Multimodal medical imaging; Deep learning models; Brain cancer diagnosis; GRU model (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:ssrchp:978-3-031-98728-1_19

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DOI: 10.1007/978-3-031-98728-1_19

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