Convolutional Neural Networks for Multimodal Brain MRI Images Segmentation: A Comparative Study

Moujahid, Hicham; Cherradi, Bouchaib; Bahatti, Lhoussain

doi:10.1007/978-3-030-45183-7_25

Cited by 28 publications

(13 citation statements)

References 14 publications

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“…This algorithm in the BraTS 2017 competition can obtain the first position between more than 50 teams. The performance of this algorithm is supreme because it combines multiple configured and trained CNN models [73]. Deep-Medic is the first employed architecture in this model; Deep-Medic is the 11-layers deep, multi-scale 3D CNN for brain lesion segmentation.…”

Section: Ensembles Of Multiple Models and Architectures (Emma)mentioning

confidence: 99%

Deep Learning for Smart Healthcare—A Survey on Brain Tumor Detection from Medical Imaging

Arabahmadi

Farahbakhsh

Rezazadeh

2022

Sensors

135

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Advances in technology have been able to affect all aspects of human life. For example, the use of technology in medicine has made significant contributions to human society. In this article, we focus on technology assistance for one of the most common and deadly diseases to exist, which is brain tumors. Every year, many people die due to brain tumors; based on “braintumor” website estimation in the U.S., about 700,000 people have primary brain tumors, and about 85,000 people are added to this estimation every year. To solve this problem, artificial intelligence has come to the aid of medicine and humans. Magnetic resonance imaging (MRI) is the most common method to diagnose brain tumors. Additionally, MRI is commonly used in medical imaging and image processing to diagnose dissimilarity in different parts of the body. In this study, we conducted a comprehensive review on the existing efforts for applying different types of deep learning methods on the MRI data and determined the existing challenges in the domain followed by potential future directions. One of the branches of deep learning that has been very successful in processing medical images is CNN. Therefore, in this survey, various architectures of CNN were reviewed with a focus on the processing of medical images, especially brain MRI images.

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Section: Ensembles Of Multiple Models and Architectures (Emma)mentioning

confidence: 99%

Deep Learning for Smart Healthcare—A Survey on Brain Tumor Detection from Medical Imaging

Arabahmadi

Farahbakhsh

Rezazadeh

2022

Sensors

135

View full text Add to dashboard Cite

show abstract

“…These smart tools constitute an important aid for medical professionals to gain time, effort, and accuracy [14,15]. The diagnosis of most diseases can be aided by ML and DL algorithms, such as brain tumor detection using the CNN technique [16,17], diabetes mellitus prediction [18,19], patients with atherosclerosis disease classification [20][21][22], and detecting pneumonia on lung images [23,24].…”

Section: Introductionmentioning

confidence: 99%

Combining CNN and Grad-Cam for COVID-19 Disease Prediction and Visual Explanation

Moujahid¹,

Cherradi²,

Al-Sarem³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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“…It makes computers capable of solving many pattern recognition and object extraction problems using datasets of 2D or 3D images. In case of 3D MRI images classification task [4], [5] it requires a huge processing capacity which can be bypassed by adopting a parallel algorithms [6]. In [7], an example of the above parallel approach where the authors propose a parallel c-mean algorithm applied to MRI images classification showing good time complexity on its results.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network Based Classification of Patients with Pneumonia using X-ray Lung Images

Moujahid¹,

Cherradi²,

Gannour³

et al. 2020

Adv. sci. technol. eng. syst. j.

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Analysis and classification of lung diseases using X-ray images is a primary step in the procedure of pneumonia diagnosis, especially in a critical period as pandemic of COVID-19 that is type of pneumonia. Therefore, an automatic method with high accuracy of classification is needed to perform classification of lung diseases due to the increasing number of cases. Convolutional Neural Networks (CNN) based classification has gained a big popularity over the last few years because of its speed and level of accuracy on the image's classification tasks. Through this article, we propose an implementation a CNNbased classification models using transfer learning technique to perform pneumonia detection and compare the results in order to detect the best model for the task according to certain parameters. As this has become a fast expanding field, there are several models but we will focus on the best outperforming algorithms according to their architecture, length and type of layers and evaluation parameters for the classification tasks. Firstly, we review the existing conventional methods and deep learning architectures used for segmentation in general. Next, we perform a deep performance and analysis based on accuracy and loss function of implemented models. A critical analysis of the results is made to highlight all important issues to improve.

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Convolutional Neural Networks for Multimodal Brain MRI Images Segmentation: A Comparative Study

Cited by 28 publications

References 14 publications

Deep Learning for Smart Healthcare—A Survey on Brain Tumor Detection from Medical Imaging

Deep Learning for Smart Healthcare—A Survey on Brain Tumor Detection from Medical Imaging

Combining CNN and Grad-Cam for COVID-19 Disease Prediction and Visual Explanation

Convolutional Neural Network Based Classification of Patients with Pneumonia using X-ray Lung Images

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