Blockchain-Based Deep CNN for Brain Tumor Prediction Using MRI Scans

Mohammad, Farah; Al-Ahmadi, Saad; Muhtadi, Jala Al

doi:10.3390/diagnostics13071229

Cited by 5 publications

(4 citation statements)

References 53 publications

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“…This approach addresses problems like as big datasets and low-quality medical pictures by increasing model capabilities while reducing processing complexity. A secure CNN model was reported by Mohammad et al 29 to predict brain cancers from MRI data. Blockchain layers and characteristics retrieved from the scans are used in the model.…”

Section: Literature Surveymentioning

confidence: 99%

“…To sum up, several recent studies applied deep learning to the classifying of brain tumors, and they achieved good results (Phaye et al 16 ; Pashaei et al 17 ; Gumaei et al 18 ; Haq et al 21 ; Noreen et al 22 ; Hashemzehi et al 23 ; Özyurt et al 24 ; Toğaçar et al 25 ; Rehman et al 26 ; Badža et al 27 ; Mohammad et al 29 ). However, a key challenge remains in existing methods: proper tumor segmentation, which is usually performed as a manual process or by less precise algorithms.…”

Section: Literature Surveymentioning

confidence: 99%

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Classification of brain tumor types through MRIs using parallel CNNs and firefly optimization

Li,

Zhang,

et al. 2024

Sci Rep

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Image segmentation is a critical and challenging endeavor in the field of medicine. A magnetic resonance imaging (MRI) scan is a helpful method for locating any abnormal brain tissue these days. It is a difficult undertaking for radiologists to diagnose and classify the tumor from several pictures. This work develops an intelligent method for accurately identifying brain tumors. This research investigates the identification of brain tumor types from MRI data using convolutional neural networks and optimization strategies. Two novel approaches are presented: the first is a novel segmentation technique based on firefly optimization (FFO) that assesses segmentation quality based on many parameters, and the other is a combination of two types of convolutional neural networks to categorize tumor traits and identify the kind of tumor. These upgrades are intended to raise the general efficacy of the MRI scan technique and increase identification accuracy. Using MRI scans from BBRATS2018, the testing is carried out, and the suggested approach has shown improved performance with an average accuracy of 98.6%.

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Section: Literature Surveymentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

Classification of brain tumor types through MRIs using parallel CNNs and firefly optimization

Li,

Zhang,

et al. 2024

Sci Rep

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“…An adaptive neuro-fuzzy system classifier for detecting brain tumor implemented with IoT through simulations was developed by ( Sandya et al, 2023 ). A deep learning method for brain tumor detection based on blockchain technology was proposed to predict using MRI images ( Mohammad et al, 2023 ).…”

Section: Multi-omics Data Integration Interpretation and Disease Pred...mentioning

confidence: 99%

A review of multi-omics data integration through deep learning approaches for disease diagnosis, prognosis, and treatment

Wekesa

Kimwele

2023

Front. Genet.

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Accurate diagnosis is the key to providing prompt and explicit treatment and disease management. The recognized biological method for the molecular diagnosis of infectious pathogens is polymerase chain reaction (PCR). Recently, deep learning approaches are playing a vital role in accurately identifying disease-related genes for diagnosis, prognosis, and treatment. The models reduce the time and cost used by wet-lab experimental procedures. Consequently, sophisticated computational approaches have been developed to facilitate the detection of cancer, a leading cause of death globally, and other complex diseases. In this review, we systematically evaluate the recent trends in multi-omics data analysis based on deep learning techniques and their application in disease prediction. We highlight the current challenges in the field and discuss how advances in deep learning methods and their optimization for application is vital in overcoming them. Ultimately, this review promotes the development of novel deep-learning methodologies for data integration, which is essential for disease detection and treatment.

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“…The final step employed M-SVM for accurate BTC, identifying meningioma, glioma, and pituitary images. [26], offering enhanced security and precision in tumor prediction, showing the promise of blockchain in medical imaging. Reza et al devised an efficient CNN-based strategy for classifying MRI-based tumors [27].…”

Section: Related Workmentioning

confidence: 99%

Efficient Skip Connections-Based Residual Network (ESRNet) for Brain Tumor Classification

B.,

Kaur,

Singh

et al. 2023

Diagnostics

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Brain tumors pose a complex and urgent challenge in medical diagnostics, requiring precise and timely classification due to their diverse characteristics and potentially life-threatening consequences. While existing deep learning (DL)-based brain tumor classification (BTC) models have shown significant progress, they encounter limitations like restricted depth, vanishing gradient issues, and difficulties in capturing intricate features. To address these challenges, this paper proposes an efficient skip connections-based residual network (ESRNet). leveraging the residual network (ResNet) with skip connections. ESRNet ensures smooth gradient flow during training, mitigating the vanishing gradient problem. Additionally, the ESRNet architecture includes multiple stages with increasing numbers of residual blocks for improved feature learning and pattern recognition. ESRNet utilizes residual blocks from the ResNet architecture, featuring skip connections that enable identity mapping. Through direct addition of the input tensor to the convolutional layer output within each block, skip connections preserve the gradient flow. This mechanism prevents vanishing gradients, ensuring effective information propagation across network layers during training. Furthermore, ESRNet integrates efficient downsampling techniques and stabilizing batch normalization layers, which collectively contribute to its robust and reliable performance. Extensive experimental results reveal that ESRNet significantly outperforms other approaches in terms of accuracy, sensitivity, specificity, F-score, and Kappa statistics, with median values of 99.62%, 99.68%, 99.89%, 99.47%, and 99.42%, respectively. Moreover, the achieved minimum performance metrics, including accuracy (99.34%), sensitivity (99.47%), specificity (99.79%), F-score (99.04%), and Kappa statistics (99.21%), underscore the exceptional effectiveness of ESRNet for BTC. Therefore, the proposed ESRNet showcases exceptional performance and efficiency in BTC, holding the potential to revolutionize clinical diagnosis and treatment planning.

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Blockchain-Based Deep CNN for Brain Tumor Prediction Using MRI Scans

Cited by 5 publications

References 53 publications

Classification of brain tumor types through MRIs using parallel CNNs and firefly optimization

Classification of brain tumor types through MRIs using parallel CNNs and firefly optimization

A review of multi-omics data integration through deep learning approaches for disease diagnosis, prognosis, and treatment

Efficient Skip Connections-Based Residual Network (ESRNet) for Brain Tumor Classification

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