Ensemble deep learning for brain tumor detection

Alsubai, Shtwai; Khan, Habib Ullah; Alqahtani, Abdullah; Sha, Mohemmed; Abbas, Sidra; Mohammad, Uzma Ghulam

doi:10.3389/fncom.2022.1005617

Cited by 47 publications

(17 citation statements)

References 44 publications

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“…In a study conducted in 2022, the convolution neural network (CNN) method was used to enhance MRI images with brain tumors, and then the results were reevaluated with the LSTM algorithm. As a result of the study, an achieved success rate was 99.1% [9]. In a study conducted in 2020, researchers examined brain tumor MRI images with the LSTM algorithm.…”

Section: Related Studiesmentioning

confidence: 99%

Brain tumor magnetic resonance images enhanced by hybrid method based on deep learning paradigm

Gül

Kaya

2023

Preprint

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The development of software engineering has given very successful results in the field of medical diagnosis in recent years. Deep learning and machine learning applications give remarkable results in the detection, monitoring, diagnosis, and treatment of possible tumoral regions with the analysis of the obtained medical images and data mining. Studies to diagnose brain tumors are essential because of the wide variety of brain tumors, the importance of the patient's survival time, and the brain tumor's aggressive nature. Brain tumors are defined as a disease with destructive and lethal features. Detection of a brain tumor is an essential process because of the difficulty in distinguishing between abnormal and normal tissues. With the right diagnosis, the patient can get excellent treatment, extending their lifespan. Despite all the research, there are still significant limitations in detecting tumor areas because of abnormal lesion distribution. It may be challenging to locate an area with very few tumor cells because areas with such small areas frequently appear healthy. Studies are becoming more common in which automated classification of early-stage brain tumors is performed using deep learning or machine learning approaches. This study proposes a hybrid deep learning model for the detection and early diagnosis of brain tumors via magnetic resonance imaging. The dataset images were subjected to Local Binary Pattern (LBP) and Long Short-Term Memory (LSTM) algorithms. The highest accuracy rate obtained in the hybrid model created is 98.66%.

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Section: Related Studiesmentioning

confidence: 99%

Brain tumor magnetic resonance images enhanced by hybrid method based on deep learning paradigm

Gül

Kaya

2023

Preprint

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“…Tere are three forms of BCI invasiveness based on the position of electrodes [32][33][34]: (1) invasive BCIs that are embedded into the brain; (2) partial invasive BCIs that have the device implanted inside the skull but outside the brain; and (3) non-invasive BCI systems uses neuron imaging outside the skull. Invasive BCIs involve the implanting of microelectrodes that are implanted in the brain [4,35].…”

Section: Invasivenessmentioning

confidence: 99%

“…Advances in technology have given hope to many individuals who suffer from chronic brain-related medical conditions [ 1 ]. Those with affective brain disorders have been given hope that a level of disability caused by their conditions can be alleviated, and they can enjoy a more “normal” life [ 2 , 3 ].…”

Section: Introductionmentioning

confidence: 99%

Identifying Thematics in a Brain‐Computer Interface Research

Alharbi

2023

Computational Intelligence and Neuroscience

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This umbrella review is motivated to understand the shift in research themes on brain-computer interfacing (BCI) and it determined that a shift away from themes that focus on medical advancement and system development to applications that included education, marketing, gaming, safety, and security has occurred. The background of this review examined aspects of BCI categorisation, neuroimaging methods, brain control signal classification, applications, and ethics. The specific area of BCI software and hardware development was not examined. A search using One Search was undertaken and 92 BCI reviews were selected for inclusion. Publication demographics indicate the average number of authors on review papers considered was 4.2 ± 1.8. The results also indicate a rapid increase in the number of BCI reviews from 2003, with only three reviews before that period, two in 1972, and one in 1996. While BCI authors were predominantly Euro-American in early reviews, this shifted to a more global authorship, which China dominated by 2020–2022. The review revealed six disciplines associated with BCI systems: life sciences and biomedicine (n = 42), neurosciences and neurology (n = 35), and rehabilitation (n = 20); (2) the second domain centred on the theme of functionality: computer science (n = 20), engineering (n = 28) and technology (n = 38). There was a thematic shift from understanding brain function and modes of interfacing BCI systems to more applied research novel areas of research-identified surround artificial intelligence, including machine learning, pre-processing, and deep learning. As BCI systems become more invasive in the lives of “normal” individuals, it is expected that there will be a refocus and thematic shift towards increased research into ethical issues and the need for legal oversight in BCI application.

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“…While existing literature primarily focuses on enhancing classification methods using advanced ML and DL techniques [26][27][28][29], the perfection of CS in MRI signal acquisition can potentially improve the overall diagnosis model in terms of both acquisition and classification. Therefore, this proposed work aims to perform classification on compressively sensed MRI and compare it with a state-of-the-art methodology for tumor classification, thus providing insights into the scope and future improvements.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning based tumor diagnosis using compressive sensing in MRI images

Mathew,

Stanley,

Jose

2023

Biomed. Phys. Eng. Express

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Despite the widespread use of Magnetic Resonance Imaging (MRI) analysis for disease diagnosis, processing and analyzing the substantial amount of acquired data may be challenging. Compressive Sensing (CS) offers a promising solution to this problem. MRI diagnosis can be performed faster and more accurately using CS since it requires fewer data for image analysis. A combination of CS with conventional and Deep Learning (DL) models, specifically VGGNet-16, is proposed for categorizing reconstructed MRI images into healthy and unhealthy. The model is properly trained using a dataset containing both normal and tumor images. The method is evaluated using a variety of parameters, including recall, F1-score, accuracy, and precision. Using the VGGNet-16 model, the proposed work achieved a classification accuracy of 98.7%, which is comparable with another state-of-the-art method based on traditionally acquired MRI images. The results indicate that CS may be useful in clinical settings for improving the efficiency and accuracy of MRI-based tumor diagnosis. Furthermore, the approach could be extended to other medical imaging modalities, possibly improving diagnosis accuracy. The study illustrates how CS can enhance medical imaging analysis, particularly in the context of tumor diagnosis using MRI images. It is necessary to conduct further research to investigate the potential applications of CS in other medical imaging contexts.

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Ensemble deep learning for brain tumor detection

Cited by 47 publications

References 44 publications

Brain tumor magnetic resonance images enhanced by hybrid method based on deep learning paradigm

Brain tumor magnetic resonance images enhanced by hybrid method based on deep learning paradigm

Identifying Thematics in a Brain‐Computer Interface Research

Machine Learning based tumor diagnosis using compressive sensing in MRI images

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