DResU-Net: 3D Deep Residual U-Net Based Brain Tumor Segmentation from Multimodal MRI

Raza, Rehan; Bajwa, Usama Ijaz; Mehmood, Yasar; Anwar, Muhammad Waqas; Jamal, Muhammad  Hassan

doi:10.2139/ssrn.4024177

Cited by 4 publications

(1 citation statement)

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“…However, this suggested model requires greater computational and hardware power. In 2023, Raza et al [33] proposed a deep learning architecture called dResU-Net, which aims to dominate the problem of vanishing gradient in deep neural networks. The suggested architecture combines the deep residual network and the U-Net model, two well-known deep learning models.…”

Section: Related Workmentioning

confidence: 99%

Advances in Brain Tumor Segmentation and Skull Stripping: A 3D Residual Attention U-Net Approach

Dawood,

Hashim,

Nasser

2023

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The timely diagnosis of brain tumors plays a critical role in enhancing patient prognosis and survival rates. Despite its superior accuracy, manual tumor segmentation is known to be a labor-intensive process. Over the years, a collection of automated tumor segmentation methodologies has been devised and investigated. However, a universally applicable resolution that consistently delivers reliable outcomes across diverse datasets continues to be elusive. Additionally, skull stripping remains a crucial prerequisite to the tumor segmentation procedure. This paper introduces an integrated 3D Attention Residual U-Net (3D_Att_Res_U-Net) model that seamlessly merges attention mechanisms and residual units within the U-Net architecture to augment the performance of brain tumor segmentation and skull stripping in Magnetic Resonance Imaging (MRI). An initial preprocessing stage is implemented, incorporating bias field correction and intensity normalization to optimize performance. The proposed model is trained using the Brain Tumor Segmentation (BraTS) 2020 dataset, along with the Neurofeedback Skull Stripping (NFBS) dataset. The proposed methodology achieved Dice Similarity Coefficients (DSC) of 0.9961 for skull stripping, and 0.9985, 0.9982, and 0.9980 for whole tumor, enhanced tumor, and tumor core segmentation, respectively. Experimental results underscore the applicability and superiority of the proposed approach compared to existing methods in this research domain.

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Section: Related Workmentioning

confidence: 99%

Advances in Brain Tumor Segmentation and Skull Stripping: A 3D Residual Attention U-Net Approach

Dawood,

Hashim,

Nasser

2023

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Formal Modeling and Analysis of Integrated Healthcare System using Colored Petri Nets

Jabbar,

Hussain,

Farooqi

et al. 2022

VFAST trans. softw. eng.

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Given today's situation, when a disease like COVID-19 has engulfed the world, the importance of the Integrated Healthcare System (HCS) increases a lot. Pakistan's healthcare system is not linked. Due to which the patients have trouble in the flow required for treatment in the existing healthcare system. This research understands and integrates the healthcare system in Pakistan. For this purpose, a formal modeling technique is used which not only helps us to integrate the system but also validate and verify the system. First of all, this research reviews existing Healthcare Systems. A comprehensive survey leads to the limitations of existing architectures. To address the limitations, this research proposes an improved architecture for the healthcare system that alleviates the issues of existing architectures. To verify the proposed architecture at the design level, a formal approach is adopted. Therefore, this architecture is modeled using a well-known technique, called Colored Petri Net (CPN). State Space Analysis after simulating the model ensures its consistency and authenticity. The properties indicate that any patient who enters the system exits without any hindrance after undergoing treatment.

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Advancing Brain MRI Image Classification: Integrating VGG16 and ResNet50 with a Multi-Verse Optimization Method

Sarshar,

Sadeghi,

Kamsari

et al. 2024

BioMed

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Background/Objectives: The accurate categorization of brain MRI images into tumor and non-tumor categories is essential for a prompt and effective diagnosis. This paper presents a novel methodology utilizing advanced Convolutional Neural Network (CNN) designs to tackle the complexity and unpredictability present in brain MRI data. Methods: The methodology commences with an extensive preparation phase that includes image resizing, grayscale conversion, Gaussian blurring, and the delineation of the brain region for preparing the MRI images for analysis. The Multi-verse Optimizer (MVO) is utilized to optimize data augmentation parameters and refine the configuration of trainable layers in VGG16 and ResNet50. The model’s generalization capabilities are significantly improved by the MVO’s ability to effectively balance computational cost and performance. Results: The amalgamation of VGG16 and ResNet50, further refined by the MVO, exhibits substantial enhancements in classification metrics. The MVO-optimized hybrid model demonstrates enhanced performance, exhibiting a well-calibrated balance between precision and recall, rendering it exceptionally trustworthy for medical diagnostic applications. Conclusions: The results highlight the effectiveness of MVO-optimized CNN models for classifying brain tumors in MRI data. Future investigations may examine the model’s applicability to multiclass issues and its validation in practical clinical environments.

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DResU-Net: 3D Deep Residual U-Net Based Brain Tumor Segmentation from Multimodal MRI

Cited by 4 publications

References 0 publications

Advances in Brain Tumor Segmentation and Skull Stripping: A 3D Residual Attention U-Net Approach

Advances in Brain Tumor Segmentation and Skull Stripping: A 3D Residual Attention U-Net Approach

Formal Modeling and Analysis of Integrated Healthcare System using Colored Petri Nets

Advancing Brain MRI Image Classification: Integrating VGG16 and ResNet50 with a Multi-Verse Optimization Method

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