Brain Tumor Detection and Multi-Grade Segmentation Through Hybrid Caps-VGGNet Model

Jabbar, Ayesha; Naseem, Shahid; Mahmood, Tariq; Saba, Tanzila; Alamri, Faten S.; Khan, Azmat Ullah

doi:10.1109/access.2023.3289224

Cited by 30 publications

(6 citation statements)

References 54 publications

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“…The proposed method outperformed existing state-of-the-art designs, achieving an impressive mean accuracy of 99.40%, 98.46%, and 98.29%, and sensitivity of 99.39%, 98.41%, and 98.25% for the tumor core on the validation set. Furthermore, Jabbar et al [24] proposed a groundbreaking hybrid model that merged two powerful neural network models, the CapsNet model and the VGGNet model, to create a more accurate and efficient system for grading brain tumor regions. The team tested the [25] Supervised learning algorithms 1.…”

Section: Related Workmentioning

confidence: 99%

“…The BraTS 2019 dataset, which consisted of 2,800 MR images, was used to assess the performance of our approach. Of these images, 1,400 were considered "1" (Tumors), and the other 1,400 scans were considered "0" (non-Tumor) [24]. The Kaggle dataset, on the other hand, is curated and labeled to make it easy to distinguish between brain tumors and non-tumors.…”

Section: A Dataset Descriptionmentioning

confidence: 99%

“…One category is for images showing brain tumors or abnormalities (class yes), while the other is for images depicting non-tumorous or healthy brain tissue (type no). The Kaggle dataset (Br35H) contains 253 images, of which 155 exhibit brain tumors while 98 are nontumorous [24]. To avoid the issue of class dominance, we equally divided the dataset between the two classes, with 70% (2,137) of the images used for training and the remaining 30% (916) used for validation.…”

Section: A Dataset Descriptionmentioning

confidence: 99%

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U-Net++DSM: Improved U-Net++ for Brain Tumor Segmentation With Deep Supervision Mechanism

Wisaeng

2023

IEEE Access

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The segmentation of brain tumors is an important and challenging content in medical image processing. Relying solely on human experts to manually segment large volumes of data can be timeconsuming and delay diagnosis. To address this challenge, researchers have set out to develop an algorithm that can automatically determine whether MRI images contain brain tumors and identify their features. This paper proposes the U-Net++DSM, a collaborative approach combining U-Net++ with Deep Supervision Mechanism (DSM) as its backbone. To enhance the segmentation power of U-Net++DSM, medical professionals have trained a dilation operator using fully annotated images. The results of this method demonstrate that the combination of U-Net++DSM and the dilation operator significantly improves segmentation accuracy, especially when the number of fully-labeled images is limited. The results show that the proposed U-Net++DSM outperforms traditional U-Net models by achieving high segmentation performance, surpassing other state-of-the-art models, with a sensitivity of 98.59.00%, a specificity of 98.72%, an accuracy of 98.64%, and an average Dice score of 98.81% when tested on publicly available databases. Compared to other existing segmentation methods, the U-Net++DSM method has the potential to yield even better brain tumor segmentation results in terms of pixel-based classification and dice similarity performance metrics.

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Section: Related Workmentioning

confidence: 99%

Section: A Dataset Descriptionmentioning

confidence: 99%

Section: A Dataset Descriptionmentioning

confidence: 99%

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U-Net++DSM: Improved U-Net++ for Brain Tumor Segmentation With Deep Supervision Mechanism

Wisaeng

2023

IEEE Access

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“…Brain tumors represent an irregular and unchecked growth of cells within the brain [1,2]. They can be either malignant (cancerous) or benign (noncancerous).…”

Section: Introductionmentioning

confidence: 99%

Multimodal Brain Tumor Detection Using Loss Aware Residual UNet Model

Kesharwani,

Shivhare

2023

IJOSCIENCE

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This research introduces a novel approach to multimodal brain tumor detection using the Residual UNet architecture. By integrating various imaging techniques, such as MRI and CT, the study offers a comprehensive perspective on brain anomalies. The Residual UNet architecture, an enhancement of the conventional UNet, is tailor-made for biomedical image segmentation. The architecture's residual connections optimize deeper network training, making it suitable for detecting intricate patterns in multimodal brain images. This paper details a structured approach that amalgamates advanced imaging techniques and machine learning to develop an efficient tumor detection system. The study utilizes the BRATS brain tumor MRI datasets and incorporates sophisticated preprocessing, data augmentation, and the innovative Loss-Aware ResUNet model for optimal results. It outperformed other top models with a peak accuracy of 97%.

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“…It is essential to acknowledge that the involvement of several domains in sharing medical data has presented challenges in managing the application effectively. Therefore, there is a necessity for a cloud-based environment that facilitates the collaborative sharing of information across multiple administrative domains [10,11]. Cloud computing has numerous advantages, one of which is the efficient and fast transfer and sharing of medical information without any disruptions.…”

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confidence: 99%

An Optimized Role-Based Access Control Using Trust Mechanism in E-Health Cloud Environment

Butt,

Mahmood,

Saba

et al. 2023

IEEE Access

Self Cite

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In today's world, services are improved and advanced in every field of life. Especially in the health sector, information technology (IT) plays a vigorous role in electronic health (e-health). To achieve benefits from e-health, its cloud-based implementation is necessary. With this environment's multiple benefits, privacy and security loopholes exist. As the number of users grows, the Electronic Healthcare System's (EHS) response time becomes slower. This study presented a trust mechanism for access control (AC) known as role-based access control (RBAC) to address this issue. This method observes the user's behavior and assigns roles based on it. The AC module has been implemented using SQL Server, and an administrator develops controls for users with roles and access to multiple EHS modules. To validate the user's trust value, A .net-based framework has been introduced. The framework of e-health proposed in this research ensures that users can protect their data from intruders and other security threats.

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Brain Tumor Detection and Multi-Grade Segmentation Through Hybrid Caps-VGGNet Model

Cited by 30 publications

References 54 publications

U-Net++DSM: Improved U-Net++ for Brain Tumor Segmentation With Deep Supervision Mechanism

U-Net++DSM: Improved U-Net++ for Brain Tumor Segmentation With Deep Supervision Mechanism

Multimodal Brain Tumor Detection Using Loss Aware Residual UNet Model

An Optimized Role-Based Access Control Using Trust Mechanism in E-Health Cloud Environment

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