Segmenting and Classifiying the Brain Tumor from MRI Medical Images Based on Machine Learning Algorithms: A Review

Kareem, Omar Sedqi; Al-Sulaifanie, Ahmed Khorsheed; Hasan, Dathar Abas; Ahmed, Dindar Mikaeel

doi:10.9734/ajrcos/2021/v10i230239

Cited by 8 publications

(5 citation statements)

References 23 publications

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“…Unsupervised learning has been used in medical imaging analysis for various tasks, such as image registration, data compression, and dimensionality reduction [ 72 , 73 ]. Some examples of unsupervised learning algorithms that have been used in medical imaging analysis include k-means clustering, principal component analysis (PCA), and autoencoders [ 74 , 75 , 76 ].…”

Section: Convolutional Neural Network Supervised Learning and Unsuper...mentioning

confidence: 99%

Artificial Intelligence Frameworks to Detect and Investigate the Pathophysiology of Spaceflight Associated Neuro-Ocular Syndrome (SANS)

Ong,

Waisberg,

Masalkhi

et al. 2023

Brain Sciences

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Spaceflight associated neuro-ocular syndrome (SANS) is a unique phenomenon that has been observed in astronauts who have undergone long-duration spaceflight (LDSF). The syndrome is characterized by distinct imaging and clinical findings including optic disc edema, hyperopic refractive shift, posterior globe flattening, and choroidal folds. SANS serves a large barrier to planetary spaceflight such as a mission to Mars and has been noted by the National Aeronautics and Space Administration (NASA) as a high risk based on its likelihood to occur and its severity to human health and mission performance. While it is a large barrier to future spaceflight, the underlying etiology of SANS is not well understood. Current ophthalmic imaging onboard the International Space Station (ISS) has provided further insights into SANS. However, the spaceflight environment presents with unique challenges and limitations to further understand this microgravity-induced phenomenon. The advent of artificial intelligence (AI) has revolutionized the field of imaging in ophthalmology, particularly in detection and monitoring. In this manuscript, we describe the current hypothesized pathophysiology of SANS and the medical diagnostic limitations during spaceflight to further understand its pathogenesis. We then introduce and describe various AI frameworks that can be applied to ophthalmic imaging onboard the ISS to further understand SANS including supervised/unsupervised learning, generative adversarial networks, and transfer learning. We conclude by describing current research in this area to further understand SANS with the goal of enabling deeper insights into SANS and safer spaceflight for future missions.

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Section: Convolutional Neural Network Supervised Learning and Unsuper...mentioning

confidence: 99%

Artificial Intelligence Frameworks to Detect and Investigate the Pathophysiology of Spaceflight Associated Neuro-Ocular Syndrome (SANS)

Ong,

Waisberg,

Masalkhi

et al. 2023

Brain Sciences

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“…In contrast, each tree constructs itself differently from other trees in the same forest and frequently chooses its features randomly. After making the forest, each decision tree in the forest determines which class (for classification) this input data belong to, then the model chooses the most specific type (majority voting) [63].…”

Section: Image Classification a Random Forest (Rf)mentioning

confidence: 99%

An Overview of Content-Based Image Retrieval Methods and Techniques

Hadid,

Hussein,

Al-Qaysi

et al. 2023

ijcsm

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With the development of Internet technology and the popularity of digital devices, Content-Based Image Retrieval (CBIR) has been quickly developed and applied in various fields related to computer vision and artificial intelligence. Currently, it is possible to retrieve related images effectively and efficiently from a large-scale database with an input image. In the past ten years, great efforts have been made for new theories and models of CBIR, and many effective CBIR algorithms have been established. Content-based image retrieval helps to discover identical images in a big dataset that match a query image. The query image's representative feature similarities to the dataset images typically assist in ranking the images for retrieval. There are various past studies on different handicraft feature descriptors according to the visual features that describe the images: color, texture, and shape. However, deep learning has been the dominant alternative to manually planned feature engineering; it automatically takes the features from the data. The current work reviews recent advancements in content-based image retrieval. For a deeper understanding of the advancement, the explanation of current state-of-the-art approaches from various vantage points is also conducted. This review employs a taxonomy encompassing various retrieval networks, classification types, and descriptors and this study will help researchers make more progress in image retrieval

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“…Li et al [71] proposed two adaptation models for recurrent language models of the Neural Network (RNNLMs) that were suggested to capture subject effects and long-distance stimuli for automated conversational speech recognition (ASR). To adjust an RNNLM, they use a Fast Marginal Adaptation (FMA) structure.…”

Section: Related Workmentioning

confidence: 99%

“…The computational and simulation results demonstrated the importance of this. Jabeen et al [71] The authors applied video retrieval based on content that uses convolution-derived genres and a recurrent network. A hundred video set of approximately 300 to 500 frames per film was utilized to extract visual features.…”

Section: Related Workmentioning

confidence: 99%

The Prediction Process Based on Deep Recurrent Neural Networks: A Review

Zeebaree

Abdulazeez

Abdullrhman

et al. 2021

AJRCoS

Self Cite

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Prediction is vital in our daily lives, as it is used in various ways, such as learning, adapting, predicting, and classifying. The prediction of parameters capacity of RNNs is very high; it provides more accurate results than the conventional statistical methods for prediction. The impact of a hierarchy of recurrent neural networks on Predicting process is studied in this paper. A recurrent network takes the hidden state of the previous layer as input and generates as output the hidden state of the current layer. Some of deep Learning algorithms can be utilized in as prediction tools in video analysis, musical information retrieval and time series applications. Recurrent networks may process examples simultaneously, maintaining a state or memory that recreates an arbitrarily long background window. Long Short-Term Memory (LSTM) and Bidirectional RNN (BRNN) are examples of recurrent networks. This paper aims to give a comprehensive assessment of predictions based on RNN. Additionally, each paper presents all relevant facts, such as dataset, method, architecture, and the accuracy of the predictions they deliver.

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Segmenting and Classifiying the Brain Tumor from MRI Medical Images Based on Machine Learning Algorithms: A Review

Cited by 8 publications

References 23 publications

Artificial Intelligence Frameworks to Detect and Investigate the Pathophysiology of Spaceflight Associated Neuro-Ocular Syndrome (SANS)

Artificial Intelligence Frameworks to Detect and Investigate the Pathophysiology of Spaceflight Associated Neuro-Ocular Syndrome (SANS)

An Overview of Content-Based Image Retrieval Methods and Techniques

The Prediction Process Based on Deep Recurrent Neural Networks: A Review

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