Deep learning based brain tumor segmentation: a survey

Complex & Intelligent Systems, Год журнала: 2022, Номер 9(1), С. 1001 - 1026

Опубликована: Июль 9, 2022

Abstract Brain tumor segmentation is one of the most challenging problems in medical image analysis. The goal brain to generate accurate delineation regions. In recent years, deep learning methods have shown promising performance solving various computer vision problems, such as classification, object detection and semantic segmentation. A number based been applied achieved results. Considering remarkable breakthroughs made by state-of-the-art technologies, we provide this survey with a comprehensive study recently developed techniques. More than 150 scientific papers are selected discussed survey, extensively covering technical aspects network architecture design, under imbalanced conditions, multi-modality processes. We also insightful discussions for future development directions.

Язык: Английский

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Diagnosis of Diabetes Mellitus Using Gradient Boosting Machine (LightGBM)

Diagnostics, Год журнала: 2021, Номер 11(9), С. 1714 - 1714

Опубликована: Сен. 19, 2021

Diabetes mellitus (DM) is a severe chronic disease that affects human health and has high prevalence worldwide. Research shown half of the diabetic people throughout world are unaware they have DM its complications increasing, which presents new research challenges opportunities. In this paper, we propose preemptive diagnosis method for diabetes to assist or complement early recognition in countries with low medical expert densities. data collected from Zewditu Memorial Hospital (ZMHDD) Addis Ababa, Ethiopia. Light Gradient Boosting Machine (LightGBM) one most recent successful findings gradient boosting framework uses tree-based learning algorithms. It computational complexity and, therefore, suited applications limited capacity regions such as Thus, study, apply principle LightGBM develop an accurate model diabetes. The experimental results show prepared dataset informative predict condition mellitus. With accuracy, AUC, sensitivity, specificity 98.1%, 99.9%, 96.3%, respectively, outperformed KNN, SVM, NB, Bagging, RF, XGBoost case ZMHDD dataset.

Язык: Английский

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Edge U-Net: Brain tumor segmentation using MRI based on deep U-Net model with boundary information

Expert Systems with Applications, Год журнала: 2022, Номер 213, С. 118833 - 118833

Опубликована: Сен. 22, 2022

Язык: Английский

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Brain Tumor Detection and Classification Using Intelligence Techniques: An Overview

IEEE Access, Год журнала: 2023, Номер 11, С. 12870 - 12886

Опубликована: Янв. 1, 2023

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

Язык: Английский

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Brain Tumor Diagnosis Using Machine Learning, Convolutional Neural Networks, Capsule Neural Networks and Vision Transformers, Applied to MRI: A Survey

Journal of Imaging, Год журнала: 2022, Номер 8(8), С. 205 - 205

Опубликована: Июль 22, 2022

Management of brain tumors is based on clinical and radiological information with presumed grade dictating treatment. Hence, a non-invasive assessment tumor paramount importance to choose the best treatment plan. Convolutional Neural Networks (CNNs) represent one effective Deep Learning (DL)-based techniques that have been used for diagnosis. However, they are unable handle input modifications effectively. Capsule neural networks (CapsNets) novel type machine learning (ML) architecture was recently developed address drawbacks CNNs. CapsNets resistant rotations affine translations, which beneficial when processing medical imaging datasets. Moreover, Vision Transformers (ViT)-based solutions very proposed issue long-range dependency in This survey provides comprehensive overview classification segmentation techniques, focus ML-based, CNN-based, CapsNet-based, ViT-based techniques. The highlights fundamental contributions recent studies performance state-of-the-art we present an in-depth discussion crucial issues open challenges. We also identify some key limitations promising future research directions. envisage this shall serve as good springboard further study.

Язык: Английский

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A review on brain tumor segmentation based on deep learning methods with federated learning techniques

Computerized Medical Imaging and Graphics, Год журнала: 2023, Номер 110, С. 102313 - 102313

Опубликована: Ноя. 24, 2023

Brain tumors have become a severe medical complication in recent years due to their high fatality rate. Radiologists segment the tumor manually, which is time-consuming, error-prone, and expensive. In years, automated segmentation based on deep learning has demonstrated promising results solving computer vision problems such as image classification segmentation. recently prevalent task imaging determine location, size, shape using methods. Many researchers worked various machine approaches most optimal solution convolutional methodology. this review paper, we discuss effective techniques datasets that are widely used publicly available. We also proposed survey of federated methodologies enhance global performance ensure privacy. A comprehensive literature suggested after studying more than 100 papers generalize multi-modality information. Finally, concentrated unsolved brain client-based model training strategy. Based review, future will understand path solve these issues.

Язык: Английский

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Research on tumors segmentation based on image enhancement method

Applied and Computational Engineering, Год журнала: 2024, Номер 67(1), С. 334 - 340

Опубликована: Авг. 14, 2024

One of the most effective ways to treat liver cancer is perform precise resection surgery, key step which includes digital image segmentation and its tumor. However, traditional parenchymal techniques often face several challenges in performing segmentation: lack precision, slow processing speed, computational burden. These shortcomings limit efficiency surgical planning execution. In this work, model initially describes detail a new enhancement algorithm that enhances features an by adaptively adjusting contrast brightness image. Then, deep learning-based network was introduced, specially trained on enhanced images optimize detection accuracy tumor regions. addition, multi-scale analysis have been incorporated into study, allowing analyze at different resolutions capture more nuanced features. presentation experimental results, study used 3Dircadb dataset test effectiveness proposed method. The results show compared with method, method using technology has significantly improved recall rate identification.

Язык: Английский

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Comparative analysis for accurate multi-classification of brain tumor based on significant deep learning models

Computers in Biology and Medicine, Год журнала: 2025, Номер 188, С. 109872 - 109872

Опубликована: Фев. 18, 2025

Язык: Английский

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A survey of image encryption algorithms based on chaotic system

The Visual Computer, Год журнала: 2022, Номер 39(5), С. 1975 - 2003

Опубликована: Апрель 7, 2022

Язык: Английский

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A new brain tumor diagnostic model: Selection of textural feature extraction algorithms and convolution neural network features with optimization algorithms

Computers in Biology and Medicine, Год журнала: 2022, Номер 148, С. 105857 - 105857

Опубликована: Июль 16, 2022

Язык: Английский

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