Enhanced 3D brain tumor segmentation using assortedprecision training

Pandya, Manish R; Oguine, Ozioma Collins; Bhargava, Harita; Zade, Shrikant

doi:10.54646/bijiam.2022.10

Cited by 2 publications

(1 citation statement)

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“…The development and application of deep learning methodologies have played a significant role in advancing computer vision endeavours, particularly in the domain of healthcare, specifically in the segmentation of brain tumors. Deep learning techniques have significantly transformed multiple domains of image analysis and pattern recognition, showcasing remarkable efficacy across a diverse array of applications [20], [21], [22]. Deep learning (DL) techniques have demonstrated potential in the field of healthcare for addressing challenges associated with the interpretation of medical images, including the segmentation of brain tumors [23], [24], [25].…”

Section: Introductionmentioning

confidence: 99%

Dual Vision Transformer-DSUNET With Feature Fusion for Brain Tumor Segmentation

Zakariah,

Al-Razgan,

Alfakih

2024

Preprint

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Brain tumors are one of the leading causes of cancer death; screening early is the best strategy to diagnose and treat brain tumors. Magnetic Resonance Imaging (MRI) is extensively utilized for brain tumor diagnosis; nevertheless, achieving improved accuracy and performance, which is a critical challenge in most of the previously reported automated medical diagnostics, is a difficult problem. The study introduces the Dual Vision Transformer-DSUNET model, which incorporates feature fusion techniques to provide precise and efficient differentiation between brain tumors and other brain regions by leveraging multi-modal MRI data. The impetus for this study arises from the necessity of automating the segmentation process of brain tumors in medical imaging, a critical component in the realms of diagnosis and therapy strategy. To tackle this issue the BRATS 2020 dataset is employed, an extensively utilized dataset for the segmentation of brain tumors. This dataset encompasses multi-modal MRI images, including T1-weighted, T2-weighted, T1Gd (contrast-enhanced), and FLAIR modalities. The proposed model incorporates the dual vision idea to comprehensively capture the heterogeneous properties of brain tumors across several imaging modalities. Moreover, the utilization of feature fusion techniques is implemented to augment the amalgamation of data originating from several modalities, hence enhancing the accuracy and dependability of tumor segmentation. The evaluation of the Dual Vision Transformer-DSUNET model's performance is conducted by employing the Dice Coefficient as a prevalent metric for quantifying segmentation accuracy. The results obtained from the experiment exhibit remarkable performance, with Dice Coefficient values of 91.47% for enhanced tumors, 92.38% for core tumors, and 90.88% for edema. The cumulative Dice score for the entirety of the classes is 91.29%. In addition, the model has a high level of accuracy, roughly 99.93%, which underscores its durability and efficacy in the task of segmenting brain tumors. Experimental findings demonstrate the integrity of the suggested architecture, which has quickly improved the detection accuracy of many brain diseases.

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