MRI-based Diagnosis of Brain Tumours Using a Deep Neural Network Framework

Acharya, Milan; Alsadoon, Abeer; Al-Janabi, Shahd; Prasad, P.W.C.; Dawoud, Ahmed; Alsadoon, Ghossoon; Paul, Manoranjan

doi:10.1109/citisia50690.2020.9371831

Cited by 7 publications

(2 citation statements)

References 9 publications

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“…It captures feature dependencies in both spatial and channel dimensions, surpassing other brain tumor detection methods in classification accuracy. Achraya et al [21] developed a Deep Neural Network (DNN) model for MRI brain tumor segmentation. Their approach outperformed existing models in both accuracy (90% vs. 78%) and processing time (34 ms vs. 73 ms).…”

Section: Related Workmentioning

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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Section: Related Workmentioning

confidence: 99%

Multimodal Brain Tumor Detection Using Loss Aware Residual UNet Model

Kesharwani,

Shivhare

2023

IJOSCIENCE

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“…Nowadays Neural Network based segmentation gives prominent outcomes, andthe flow of employing this model is augmenting day by day. Yantao et al [1] resembled Histogram based segmentation technique. Regarding the brain tumor segmentation task as a three-class Interpretability of machine learning models in medical applications is essential for gaining the trust of healthcare professionals.…”

Section: IImentioning

confidence: 99%

Brain Tumor Detection Using Machine Learning with CNN Algorithm

Birajdar

2023

IJRASET

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Brain tumors are a major global health issue, and successful treatment frequently depends on an early and precise diagnosis. Traditional methods of brain tumor detection, such as manual interpretation of medical images, can be timeconsuming and prone to human error. Machine learning techniques have emerged as a promising approach to assist medical professionals in the early detection and classification of brain tumors. This study presents a novel method for brain tumor detection utilizing machine learning algorithms .The dataset used in this research comprises a collection of brain MRI (Magnetic Resonance Imaging)scans from diverse sources, including both tumor and non-tumor cases. We preprocess the data by enhancing image quality and for classification, different machine learning techniques are used, such as random forests, support vector machines (SVMs), and convolutional neural networks (CNNs).

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Meta-heuristic-based FCM-UNet segmentation with multi-objective function and deep learning for brain tumour classification

Kumar¹,

Kumar

2022

Computer Methods in Biomechanics and Biomedical Engineering: Im

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MRI-based Diagnosis of Brain Tumours Using a Deep Neural Network Framework

Cited by 7 publications

References 9 publications

Multimodal Brain Tumor Detection Using Loss Aware Residual UNet Model

Multimodal Brain Tumor Detection Using Loss Aware Residual UNet Model

Brain Tumor Detection Using Machine Learning with CNN Algorithm

Meta-heuristic-based FCM-UNet segmentation with multi-objective function and deep learning for brain tumour classification

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