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Review
. 2022 Mar 2;22(5):1960.
doi: 10.3390/s22051960.

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

Mahsa Arabahmadi  1 , Reza Farahbakhsh  2 , Javad Rezazadeh  1   3
Affiliations
Review

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

Mahsa Arabahmadi et al. Sensors (Basel). .

Abstract

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.

Keywords: CNN; GAN; MRI; brain tumor classification; deep neural networks; smart healthcare; transfer learning.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The structure of this survey.
Figure 2
Figure 2
Deep learning in healthcare.
Figure 3
Figure 3
Flow diagram of the CAD system.
Figure 4
Figure 4
CNN layers, consist of 7 layers, input: [[CONV to RELU] × 2 to pool] × 3 to FC.
Figure 5
Figure 5
A representation of GAN network.
See this image and copyright information in PMC

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