MRI brain tumor medical images analysis using deep learning techniques: a systematic review

Al-Galal, Sabaa Ahmed Yahya; Alshaikhli, Imad Fakhri Taha; Abdulrazzaq, M. M.

doi:10.1007/s12553-020-00514-6

Cited by 29 publications

(11 citation statements)

References 129 publications

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“…Reference [24] analyzing the MRI brain images using different deep learning techniques (DL). The DL techniques are utilized to investigating the medical images according to the contexts, and the relevant clinical information's are obtained to making the clinical decisions.…”

Section: Related Workmentioning

confidence: 99%

Adam Optimized Deep Learning Model for Segmenting ROI Region in Medical Imaging

Jaber

Khalil

Ali

2021

Proceedings of International Conference on Emerging Technologies and Intelligent Systems

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Medical image processing played a crucial role in healthcare sectors because it produces visible images of inner body structure and internal tissues. With medical imaging, the organs' functions are analyzed, and anomalies are detected effectively. The imaging process utilizes enhancement, analysis, and recognition procedures that maximize disease detection accuracy. However, the gathered medical images have noise while performing the medical research that affects the ROI region segmentation process. The improper identification of the ROI region causes to reduce disease detection accuracy and create complexity. Therefore, this paper uses the Adam optimization technique with deep learning approaches for examining the medical images. The deep learning (DL) approaches utilizing the multiple layers, expert-tuned parameters, and learning function to deriving the affected ROT region. The DL method automatically extracts the medical imaging features that are optimized according to the RMS prop and momentum-based learning parameters. The optimized parameter minimizes the complexity while deriving the disease affected region. This process ensures high region segmentation accuracy with minimum time. The process has detected the affected region with 99.27% of accuracy.

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Section: Related Workmentioning

confidence: 99%

Adam Optimized Deep Learning Model for Segmenting ROI Region in Medical Imaging

Jaber

Khalil

Ali

2021

Proceedings of International Conference on Emerging Technologies and Intelligent Systems

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“…To mention a few, these image modalities include X-ray radiography, magnetic resonance imaging (MRI), positron emission tomography (PET), computed tomography (CT) scan, and single-photon emission computed tomography (SPECT). Since 2010s, new computational analyses of medical images have been made possible through machine learning [ 1 – 3 ]. In the meanwhile, open access medical images have become available online on several data repositories.…”

Section: Introductionmentioning

confidence: 99%

Ten quick tips for computational analysis of medical images

Chicco

Shiradkar

2023

PLoS Comput Biol

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Medical imaging is a great asset for modern medicine, since it allows physicians to spatially interrogate a disease site, resulting in precise intervention for diagnosis and treatment, and to observe particular aspect of patients’ conditions that otherwise would not be noticeable. Computational analysis of medical images, moreover, can allow the discovery of disease patterns and correlations among cohorts of patients with the same disease, thus suggesting common causes or providing useful information for better therapies and cures. Machine learning and deep learning applied to medical images, in particular, have produced new, unprecedented results that can pave the way to advanced frontiers of medical discoveries. While computational analysis of medical images has become easier, however, the possibility to make mistakes or generate inflated or misleading results has become easier, too, hindering reproducibility and deployment. In this article, we provide ten quick tips to perform computational analysis of medical images avoiding common mistakes and pitfalls that we noticed in multiple studies in the past. We believe our ten guidelines, if taken into practice, can help the computational–medical imaging community to perform better scientific research that eventually can have a positive impact on the lives of patients worldwide.

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“…More specifically, it has been restricted to only detecting brain tumors (binary classification as normal or tumor) using MRI datasets in 2020 and 2021. The research before 2020 is covered by several published papers [19]- [21]. In contrast, the limited research during 2021 to diagnose brain tumors as binary classification rather than multi-classification leads to including the research of 2020.…”

Section: Introductionmentioning

confidence: 99%

A review on detecting brain tumors using deep learning and magnetic resonance images

Al-Ani

Al-Shamma

2023

IJECE

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Early detection and treatment in the medical field offer a critical opportunity to survive people. However, the brain has a significant role in human life as it handles most human body activities. Accurate diagnosis of brain tumors dramatically helps speed up the patient's recovery and the cost of treatment. Magnetic resonance imaging (MRI) is a commonly used technique due to the massive progress of artificial intelligence in medicine, machine learning, and recently, deep learning has shown significant results in detecting brain tumors. This review paper is a comprehensive article suitable as a starting point for researchers to demonstrate essential aspects of using deep learning in diagnosing brain tumors. More specifically, it has been restricted to only detecting brain tumors (binary classification as normal or tumor) using MRI datasets in 2020 and 2021. In addition, the paper presents the frequently used datasets, convolutional neural network architectures (standard and designed), and transfer learning techniques. The crucial limitations of applying the deep learning approach, including a lack of datasets, overfitting, and vanishing gradient problems, are also discussed. Finally, alternative solutions for these limitations are obtained.

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MRI brain tumor medical images analysis using deep learning techniques: a systematic review

Cited by 29 publications

References 129 publications

Adam Optimized Deep Learning Model for Segmenting ROI Region in Medical Imaging

Adam Optimized Deep Learning Model for Segmenting ROI Region in Medical Imaging

Ten quick tips for computational analysis of medical images

A review on detecting brain tumors using deep learning and magnetic resonance images

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