A comprehensive review on brain tumor segmentation and classification of MRI images

Rao, Champakamala Sundar; Karunakara, K.

doi:10.1007/s11042-020-10443-1

Cited by 55 publications

(16 citation statements)

References 86 publications

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“…The attention mechanism idea is supported by a number of theories, including Seq2Seq models, encoders, decoders, hidden states, context vectors, and others. Some of the suggestions and possible improvements made by the published review articles includes: Further practice of hybrid-based learning technique is important to obtain strong CAD system [88], Noise estimation is challenging in machine learning and in deep learning lack of interpretability [89], How effectively automatic methods can manage the impact of treatment effects is still being researched [90], Technical issues stemming from the difficulty in defining exactly what deep learning is due to the lack of mathematical and theoretical foundations for many of its core models and techniques [91], Research should carefully consider how to lessen or compensate for observer, spectrum, and selection biases, as well as how to increase reporting transparency [92], Research should focus on optimization technique which will decide number of layers and filters in the model [93], Semi supervised training gives weak performance [94], Absences of transfer learning mechanism leads to weak generalization ability [95], Deficiency of training data and no resolution gives poor performance of CNN [96], With large volume of data quality of image segmentation needed to improved [97], Transfer learning model is required incapacitating overfitting of image [98], Accurate analysis is difficult for vast number of images [99], and Computation is difficult with multiple task [100].…”

Section: F Future Research Directionsmentioning

confidence: 99%

Brain Tumor Detection and Classification Using Intelligence Techniques: An Overview

Solanki¹,

Singh

Chouhan

et al. 2023

IEEE Access

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A tumor is carried on by rapid and uncontrolled cell growth in the brain. If it is not treated in the initial phases, it could prove fatal. Despite numerous significant efforts and encouraging outcomes, accurate segmentation and classification continue to be a challenge. Detection of brain tumors is significantly complicated by the distinctions in tumor position, structure, and proportions. The main disinterest of this study stays to offer investigators, comprehensive literature on Magnetic Resonance (MR) imaging's ability to identify brain tumors. Using computational intelligence and statistical image processing techniques, this research paper proposed several ways to detect brain cancer and tumors. This study also shows an assessment matrix for a specific system using particular systems and dataset types. This paper also explains the morphology of brain tumors, accessible data sets, augmentation methods, component extraction, and categorization among Deep Learning (DL), Transfer Learning (TL), and Machine Learning (ML) models. Finally, our study compiles all relevant material for the identification of understanding tumors, including their benefits, drawbacks, advancements, and upcoming trends.

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Section: F Future Research Directionsmentioning

confidence: 99%

Brain Tumor Detection and Classification Using Intelligence Techniques: An Overview

Solanki¹,

Singh

Chouhan

et al. 2023

IEEE Access

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“…Lower-level tumors (e.g., meningioma) are classified as grade 1 and 2, whereas more severe tumors are classified as grade 3 and 4. (e.g., glioma) (Rao and Karunakara, 2021). Glioblastoma, pituitary, and Meningioma tumors have incidence rates of 45 percent, 15 percent, and 15 percent, respectively, in clinical practice.…”

Section: Introductionmentioning

confidence: 99%

Brain Tumor Detection and Localization: An Inception V3 - Based Classification Followed By RESUNET-Based Segmentation Approach

Rastogi

Johri

Tiwari

2023

Int. j. math. eng. manag. sci.

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Adults and children alike are at risk from brain tumors. Accurate and prompt detection, on the other hand, can save lives. This research focuses on the identification and localization of brain tumors. Many research has been available on the analysis and classification of brain tumors, but only a few have addressed the issue of feature engineering. To address the difficulties of manual diagnostics and traditional feature-engineering procedures, new methods are required. To reliably segment and identify brain tumors, an automated diagnostic method is required. While progress is being made, automated brain tumor diagnosis still confront hurdles such as low accuracy and a high rate of false-positive outcomes. Deep learning is used to analyse brain tumors in the model described in this work, which improves classification and segmentation. Using Inception-V3 and RESUNET, deep learning is pragmatic for tumor classification and segmentation. On the Inception V3 model, add one extra layer as a head for classifying. The outcomes of these procedures are compared to those of existing methods. The test accuracy of the Inception-V3 with extra classification layer model is 0.9996, while the loss value is 0.0025. The model tversky value for localization and detection is 0.9688, while the model accuracy is 0.9700.

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“…Based on the above discussion, recently, several CAD methods have been introduced ( Mohan and Subashini, 2018 ; Rao and Karunakara, 2021 ). We discuss a few of the latest strategies and summarize the techniques’ limitations.…”

Section: Introductionmentioning

confidence: 99%

Design of a medical decision-supporting system for the identification of brain tumors using entropy-based thresholding and non-local texture features

Reddy

Batchu²,

Polinati³

et al. 2023

Front. Hum. Neurosci.

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IntroductionBrain tumors arise due to abnormal growth of cells at any brain location with uneven boundaries and shapes. Usually, they proliferate rapidly, and their size increases by approximately 1.4% a day, resulting in invisible illness and psychological and behavioral changes in the human body. It is one of the leading causes of the increase in the mortality rate of adults worldwide. Therefore, early prediction of brain tumors is crucial in saving a patient’s life. In addition, selecting a suitable imaging sequence also plays a significant role in treating brain tumors. Among available techniques, the magnetic resonance (MR) imaging modality is widely used due to its noninvasive nature and ability to represent the inherent details of brain tissue. Several computer-assisted diagnosis (CAD) approaches have recently been developed based on these observations. However, there is scope for improvement due to tumor characteristics and image noise variations. Hence, it is essential to establish a new paradigm.MethodsThis paper attempts to develop a new medical decision-support system for detecting and differentiating brain tumors from MR images. In the implemented approach, initially, we improve the contrast and brightness using the tuned single-scale retinex (TSSR) approach. Then, we extract the infected tumor region(s) using maximum entropy-based thresholding and morphological operations. Furthermore, we obtain the relevant texture features based on the non-local binary pattern (NLBP) feature descriptor. Finally, the extracted features are subjected to a support vector machine (SVM), K-nearest neighbors (KNN), random forest (RF), and GentleBoost (GB).ResultsThe presented CAD model achieved 99.75% classification accuracy with 5-fold cross-validation and a 91.88% dice similarity score, which is higher than the existing models.DiscussionsBy analyzing the experimental outcomes, we conclude that our method can be used as a supportive clinical tool for physicians during the diagnosis of brain tumors.

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A comprehensive review on brain tumor segmentation and classification of MRI images

Cited by 55 publications

References 86 publications

Brain Tumor Detection and Classification Using Intelligence Techniques: An Overview

Brain Tumor Detection and Classification Using Intelligence Techniques: An Overview

Brain Tumor Detection and Localization: An Inception V3 - Based Classification Followed By RESUNET-Based Segmentation Approach

Design of a medical decision-supporting system for the identification of brain tumors using entropy-based thresholding and non-local texture features

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