Brain Tumor Auto-Segmentation on Multimodal Imaging Modalities Using Deep Neural Network

Hossain, M. E.; Hossain, Md. Shazzad; Hossain, Md. Selim; Jannat, Sabila Al; Huda, Moontahina; Alsharif, Sameer; Faragallah, Osama S.; Eid, Mahmoud M. A.; Rashed, Ahmed Nabih Zaki

doi:10.32604/cmc.2022.025977

Cited by 36 publications

(2 citation statements)

References 17 publications

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“…Nowadays, deep learning (DL) has garnered significant attention in the realm of tumor segmentation and cancer detections [10]- [12]. In DL domain, CNN based methods have been investigated extensively ion health monitoring and medial image [13], [14], owing to their capacity to automatically learn relevant features from mammographic images, reducing the reliance on handcrafted features and achieving impressive results [15], [16].…”

Section: Introductionmentioning

confidence: 99%

A Single Stage Detector for Breast Cancer Detection on Digital Mammogram

Xu,

Jia,

Zhang

2024

IJACSA

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Medical image processing plays a pivotal role in modern healthcare, and the early detection of breast cancer in digital mammograms. Several methods have been explored in the literature to improve breast cancer detection, with deep-learning approaches emerging as particularly promising due to their ability to provide accurate results. However, a persistent research challenge in deep learning-based breast cancer detection lies in addressing the historically low accuracy rates observed in previous studies. This paper presents a novel deeplearning model utilizing a single-stage detector based on the YOLOv5 algorithm, designed specifically to tackle the issue of low accuracy in breast cancer detection. The proposed method involves the generation of a custom dataset and subsequent training, validation, and testing phases to evaluate the model's performance rigorously. Experimental results and comprehensive performance evaluations demonstrate that the proposed method achieves remarkable accuracy, marking a significant advancement in breast cancer detection through extensive experiments and rigorous performance analysis.

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Section: Introductionmentioning

confidence: 99%

A Single Stage Detector for Breast Cancer Detection on Digital Mammogram

Xu,

Jia,

Zhang

2024

IJACSA

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“…According to estimates, 131.5 million individuals worldwide would be affected by AD in 2050 [4][5][6][7]. The global prevalence of AD is worrying because every three seconds one person is affected by this disease.…”

Section: Introductionmentioning

confidence: 99%

Xception-Fractalnet: Hybrid Deep Learning Based Multi-Class Classification of Alzheimer’s Disease

M.¹,

Rao²

2023

Computers, Materials &Amp; Continua

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Neurological disorders such as Alzheimer's disease (AD) are very challenging to treat due to their sensitivity, technical challenges during surgery, and high expenses. The complexity of the brain structures makes it difficult to distinguish between the various brain tissues and categorize AD using conventional classification methods. Furthermore, conventional approaches take a lot of time and might not always be precise. Hence, a suitable classification framework with brain imaging may produce more accurate findings for early diagnosis of AD. Therefore in this paper, an effective hybrid Xception and Fractalnet-based deep learning framework are implemented to classify the stages of AD into five classes. Initially, a network based on Unet++ is built to segment the tissues of the brain. Then, using the segmented tissue components as input, the Xception-based deep learning technique is employed to extract high-level features. Finally, the optimized Fractalnet framework is used to categorize the disease condition using the acquired characteristics. The proposed strategy is tested on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset that accurately segments brain tissues with a 98.45% of dice similarity coefficient (DSC). Additionally, for the multiclass classification of AD, the suggested technique obtains an accuracy of 99.06%. Moreover, ANOVA statistical analysis is also used to evaluate if the groups are significant or not. The findings show that the suggested model outperforms various stateof-the-art methods in terms of several performance metrics.

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Brain tumor detection and classification in MRI using hybrid ViT and GRU model with explainable AI in Southern Bangladesh

Ahmed,

Hossain,

Islam

et al. 2024

Sci Rep

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Brain tumor, a leading cause of uncontrolled cell growth in the central nervous system, presents substantial challenges in medical diagnosis and treatment. Early and accurate detection is essential for effective intervention. This study aims to enhance the detection and classification of brain tumors in Magnetic Resonance Imaging (MRI) scans using an innovative framework combining Vision Transformer (ViT) and Gated Recurrent Unit (GRU) models. We utilized primary MRI data from Bangabandhu Sheikh Mujib Medical College Hospital (BSMMCH) in Faridpur, Bangladesh. Our hybrid ViT-GRU model extracts essential features via ViT and identifies relationships between these features using GRU, addressing class imbalance and outperforming existing diagnostic methods. We extensively processed the dataset, and then trained the model using various optimizers (SGD, Adam, AdamW) and evaluated through rigorous 10-fold cross-validation. Additionally, we incorporated Explainable Artificial Intelligence (XAI) techniques-Attention Map, SHAP, and LIME-to enhance the interpretability of the model’s predictions. For the primary dataset BrTMHD-2023, the ViT-GRU model achieved precision, recall, and F1-score metrics of 97%. The highest accuracies obtained with SGD, Adam, and AdamW optimizers were 81.66%, 96.56%, and 98.97%, respectively. Our model outperformed existing Transfer Learning models by 1.26%, as validated through comparative analysis and cross-validation. The proposed model also shows excellent performances with another Brain Tumor Kaggle Dataset outperforming the existing research done on the same dataset with 96.08% accuracy. The proposed ViT-GRU framework significantly improves the detection and classification of brain tumors in MRI scans. The integration of XAI techniques enhances the model’s transparency and reliability, fostering trust among clinicians and facilitating clinical application. Future work will expand the dataset and apply findings to real-time diagnostic devices, advancing the field.

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Brain Tumor Auto-Segmentation on Multimodal Imaging Modalities Using Deep Neural Network

Cited by 36 publications

References 17 publications

A Single Stage Detector for Breast Cancer Detection on Digital Mammogram

A Single Stage Detector for Breast Cancer Detection on Digital Mammogram

Xception-Fractalnet: Hybrid Deep Learning Based Multi-Class Classification of Alzheimer’s Disease

Brain tumor detection and classification in MRI using hybrid ViT and GRU model with explainable AI in Southern Bangladesh

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