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Journal articles on the topic 'Brain tumor segmentation dataset'

Author: Grafiati

Published: 7 June 2025

Last updated: 16 July 2025

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1

Jwaid, Wasan M., Zainab Shaker Matar Al-Husseini, and Ahmad H. Sabry. "Development of brain tumor segmentation of magnetic resonance imaging (MRI) using U-Net deep learning." Eastern-European Journal of Enterprise Technologies 4, no. 9(112) (2021): 23–31. http://dx.doi.org/10.15587/1729-4061.2021.238957.

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Brain tumors are the growth of abnormal cells or a mass in a brain. Numerous kinds of brain tumors were discovered, which need accurate and early detection techniques. Currently, most diagnosis and detection methods rely on the decision of neuro-specialists and radiologists to evaluate brain images, which may be time-consuming and cause human errors. This paper proposes a robust U-Net deep learning Convolutional Neural Network (CNN) model that can classify if the subject has a tumor or not based on Brain Magnetic resonance imaging (MRI) with acceptable accuracy for medical-grade application. T

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Wasan, M. Jwaid, Shaker Matar Al-Husseini Zainab, and H. Sabry Ahmad. "Development of brain tumor segmentation of magnetic resonance imaging (MRI) using U-Net deep learning." Eastern-European Journal of Enterprise Technologies 4, no. 9 (112) (2021): 23–31. https://doi.org/10.15587/1729-4061.2021.238957.

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Brain tumors are the growth of abnormal cells or a mass in a brain. Numerous kinds of brain tumors were discovered, which need accurate and early detection techniques. Currently, most diagnosis and detection methods rely on the decision of neuro-specialists and radiologists to evaluate brain images, which may be time-consuming and cause human errors. This paper proposes a robust U-Net deep learning Convolutional Neural Network (CNN) model that can classify if the subject has a tumor or not based on Brain Magnetic resonance imaging (MRI) with acceptable accuracy for medical-grade applicati

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Zhang, Rong, Hongliang Luo, Weijie Chen, and Yongqiang Bai. "Review of deep learning-driven MRI brain tumor detection and segmentation methods." Advances in Computer, Signals and Systems 7, no. 8 (2023): 17–28. http://dx.doi.org/10.23977/acss.2023.070803.

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The application of deep learning in the field of medical imaging has become increasingly widespread, greatly promoting the advancement and development of Magnetic Resonance Imaging (MRI) brain tumor detection and segmentation techniques. Therefore, a comprehensive review of deep learning-based methods for MRI brain tumor detection and segmentation was conducted. This review introduces the basic concepts of brain tumors and MRI brain tumor detection and segmentation, discusses the specific applications and typical methods of deep learning in MRI brain tumor detection and segmentation, and analy

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Jyoti, Kataria Supriya P. Panda. "HybridCSF model for magnetic resonance image based brain tumor segmentation." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 3 (2024): 1845–52. https://doi.org/10.11591/ijeecs.v35.i3.pp1845-1852.

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The human brain comprises a complex interconnection of nerve cells and vital organs, which regulates crucial bodily processes. Although neurons commonly undergo developmental stages, they may occasionally experience abnormalities, leading to abnormal growths known as brain tumors. The objective of brain tumor segmentation is to produce precise boundaries of brain tumor regions. This study extensively analyzes deep learning methods for brain tumor detection, evaluating their effectiveness across diverse datasets. It introduces a hybrid model, which is proposed by the name HybriCSF: hybrid convo

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Alshomrani, Faisal. "A Unified Pipeline for Simultaneous Brain Tumor Classification and Segmentation Using Fine-Tuned CNN and Residual UNet Architecture." Life 14, no. 9 (2024): 1143. http://dx.doi.org/10.3390/life14091143.

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In this paper, I present a comprehensive pipeline integrating a Fine-Tuned Convolutional Neural Network (FT-CNN) and a Residual-UNet (RUNet) architecture for the automated analysis of MRI brain scans. The proposed system addresses the dual challenges of brain tumor classification and segmentation, which are crucial tasks in medical image analysis for precise diagnosis and treatment planning. Initially, the pipeline preprocesses the FigShare brain MRI image dataset, comprising 3064 images, by normalizing and resizing them to achieve uniformity and compatibility with the model. The FT-CNN model

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Huang, Jacky, Powell Molleti, Michael Iv, Richard Lee, and Haruka Itakura. "Deep learning-based brain tumor segmentation on limited sequences of magnetic resonance imaging." Journal of Clinical Oncology 40, no. 16_suppl (2022): 2054. http://dx.doi.org/10.1200/jco.2022.40.16_suppl.2054.

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2054 Background: Deep learning algorithms trained to segment brain tumors from magnetic resonance imaging (MRI) perform well in ideal conditions supplied by curated datasets. An example is the MICCAI BraTS 2018 dataset, which provides a set of four MRI sequences (T1, T1 post-gadolinium contrast-enhanced, T2, FLAIR) on 285 samples, along with ground truth segmentations annotated by experts. In real-world settings, however, it is not uncommon for patients to undergo MRI with a limited number of image sequence acquisitions. We examined the effect of restricting the imaging sequence set when train

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Huang, Jacky, Powell Molleti, Michael Iv, Richard Lee, and Haruka Itakura. "Deep learning-based brain tumor segmentation on limited sequences of magnetic resonance imaging." Journal of Clinical Oncology 40, no. 16_suppl (2022): 2054. http://dx.doi.org/10.1200/jco.2022.40.16_suppl.2054.

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2054 Background: Deep learning algorithms trained to segment brain tumors from magnetic resonance imaging (MRI) perform well in ideal conditions supplied by curated datasets. An example is the MICCAI BraTS 2018 dataset, which provides a set of four MRI sequences (T1, T1 post-gadolinium contrast-enhanced, T2, FLAIR) on 285 samples, along with ground truth segmentations annotated by experts. In real-world settings, however, it is not uncommon for patients to undergo MRI with a limited number of image sequence acquisitions. We examined the effect of restricting the imaging sequence set when train

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Kataria, Jyoti, and Supriya P. Panda. "HybridCSF model for magnetic resonance image based brain tumor segmentation." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 3 (2024): 1845. http://dx.doi.org/10.11591/ijeecs.v35.i3.pp1845-1852.

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<p>The human brain comprises a complex interconnection of nerve cells and vital organs, which regulates crucial bodily processes. Although neurons commonly undergo developmental stages, they may occasionally experience abnormalities, leading to abnormal growths known as brain tumors. The objective of brain tumor segmentation is to produce precise boundaries of brain tumor regions. This study extensively analyzes deep learning methods for brain tumor detection, evaluating their effectiveness across diverse datasets. It introduces a hybrid model, which is proposed by the name HybriCSF: hyb

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Putta., Rama Krishna Veni, and Aruna Bala C. "The Multi Stage U-net Design for Brain Tumor Segmentation using Deep Learning Architecture." International Journal of Recent Technology and Engineering (IJRTE) 9, no. 3 (2020): 454–60. https://doi.org/10.5281/zenodo.5843656.

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Now a day’s diagnosis and accurate segmentation of brain tumors are critical conditions for successful treatment. The manual segmentation takes time consuming process, more cost and inaccurate. In this paper implementation of cascaded U-net segmentation Architecture are divided into substructures of brain tumor segmentation. The neural network is competent of end to end multi modal brain tumor segmentations.The Brain tumor segments are divided three categories. The tumor core (TC),the enhancing tumor(ET),the whole tumor (WT).The distinct data enhancement steps are better achievement. The

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Vinod, Manvika. "Detection of Brain Tumor." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 10 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem26485.

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Brain tumor detection and segmentation are important tasks in medical image analysis. This project is about creating an image classification model to detect whether an MRI image of a brain has a tumor or not. The model is created using Fast ai, which is a high-level deep learning library built on top of Py Torch. The dataset used in this project contains MRI images of brains with and without tumors. The model is trained using transfer learning with ResNet18 and ResNet34 as the base architectures. After training the model, it is exported and used to make predictions on new images using a simple

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Haq, Ejaz Ul, Huang Jianjun, Xu Huarong, Kang Li, and Lifen Weng. "A Hybrid Approach Based on Deep CNN and Machine Learning Classifiers for the Tumor Segmentation and Classification in Brain MRI." Computational and Mathematical Methods in Medicine 2022 (August 8, 2022): 1–18. http://dx.doi.org/10.1155/2022/6446680.

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Conventional medical imaging and machine learning techniques are not perfect enough to correctly segment the brain tumor in MRI as the proper identification and segmentation of tumor borders are one of the most important criteria of tumor extraction. The existing approaches are time-consuming, incursive, and susceptible to human mistake. These drawbacks highlight the importance of developing a completely automated deep learning-based approach for segmentation and classification of brain tumors. The expedient and prompt segmentation and classification of a brain tumor are critical for accurate

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Qu, Guangcan, Beichen Lu, Jialin Shi, et al. "Motion-artifact-augmented pseudo-label network for semi-supervised brain tumor segmentation." Physics in Medicine & Biology 69, no. 5 (2024): 055023. http://dx.doi.org/10.1088/1361-6560/ad2634.

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Abstract MRI image segmentation is widely used in clinical practice as a prerequisite and a key for diagnosing brain tumors. The quest for an accurate automated segmentation method for brain tumor images, aiming to ease clinical doctors’ workload, has gained significant attention as a research focal point. Despite the success of fully supervised methods in brain tumor segmentation, challenges remain. Due to the high cost involved in annotating medical images, the dataset available for training fully supervised methods is very limited. Additionally, medical images are prone to noise and motion

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Xing, Shuli, Zhenwei Lai, Junxiong Zhu, Wenwu He, and Guojun Mao. "Semantic Segmentation of Brain Tumors Using a Local–Global Attention Model." Applied Sciences 15, no. 11 (2025): 5981. https://doi.org/10.3390/app15115981.

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The distinctions between tumor areas and surrounding tissues are often subtle. Additionally, the morphology and size of tumors can vary significantly among different patients. These factors pose considerable challenges for the precise segmentation of tumors and subsequent diagnosis. Therefore, researchers are trying to develop an automated and accurate segmentation model. Currently, many segmentation models in deep learning rely on Convolutional Neural Network or Vision Transformer. However, Convolution-based models often fail to deliver precise segmentation results, while Transformer-based mo

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Lyu, Yu, and Xiaolin Tian. "MWG-UNet: Hybrid Transformer U-Net Model for Brain Tumor Segmentation in MRI Scans++." Bioengineering 12, no. 2 (2025): 140. https://doi.org/10.3390/bioengineering12020140.

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The accurate segmentation of brain tumors from medical images is critical for diagnosis and treatment planning. However, traditional segmentation methods struggle with complex tumor shapes and inconsistent image quality which leads to suboptimal results. To address this challenge, we propose multiple tasking Wasserstein Generative Adversarial Network U-shape Network++ (MWG-UNet++) to brain tumor segmentation by integrating a U-Net architecture enhanced with transformer layers which combined with Wasserstein Generative Adversarial Networks (WGAN) for data augmentation. The proposed model called

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Takahashi, Satoshi, Masamichi Takahashi, Manabu Kinoshita, et al. "NIMG-29. DEVELOPING AUTOMATIC SEGMENTATION METHOD FOR BRAIN TUMOR MR IMAGES THAT CAN BE USED AT MULTIPLE FACILITIES." Neuro-Oncology 22, Supplement_2 (2020): ii153—ii154. http://dx.doi.org/10.1093/neuonc/noaa215.642.

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Abstract BACKGROUND Manual segmentation of brain tumor images from a large volume of MR images generated in clinical routines is difficult and time-consuming. Hence, it is imperative to develop a machine learning model for automated segmentation of brain tumor images. PURPOSE Machine learning models for automated MR image segmentation of gliomas may be useful. However, the image differences among facilities cause performance degradation and impede successful automatic segmentation. In this study, we proposed a method to solve this issue. METHODS We used the data from the Multimodal Brain Tumor

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Chippalakatti, Shilpa, Renu Madhavi Chodavarapu, and Andhe Pallavi. "Identification and segmentation of tumor using deep learning and image segmentation algorithms." Indonesian Journal of Electrical Engineering and Computer Science 38, no. 3 (2025): 1782. https://doi.org/10.11591/ijeecs.v38.i3.pp1782-1792.

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<p>Brain tumor is a typical mass of tissue that develops when cells proliferate and divide excessively. Brain tumor perception requires a great deal of work and experience from the medical professional in order to identify the tumor's precise location. If a brain tumor is not discovered in a timely manner, it affects a person's ability to function normally and raises the death rate. This study focuses on tumor segmentation and tumor detection using magnetic resonance imaging (MRI) images. This work helps the medical professional to precisely identify the tumor location and segmentation p

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Srivaishnavi, K. R., T. Pramananda Perumal, and P. Anishiya. "Brain Tumor Prediction and Segmentation with Morphological Region-based Active Contour Model and Refinement using Boltzmann Monte Carlo Method in MRI Images." Indian Journal Of Science And Technology 17, no. 20 (2024): 2088–100. http://dx.doi.org/10.17485/ijst/v17i20.1231.

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Objectives: The primary goal of the research work is to accurately detect the precise location of the brain tumor in the radiological Magnetic Resonance Imaging (MRI) images of human brain using segmentation method. Methods: In this research work, we introduce mainly the Morphological Region-based Active Contour model and Boltzmann Monte Carlo method (MACB model), involving a comprehensive three-step methodology for the segmentation of the brain, MRI images in order to detect brain tumor. The initial step involves pre-processing which includes Gaussian filtering for noise reduction and Contras

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Pidishetti, Rohit Viswakarma, Maaz Amjad, and Victor S. Sheng. "Advanced Brain Tumor Segmentation Using SAM2-UNet." Applied Sciences 15, no. 6 (2025): 3267. https://doi.org/10.3390/app15063267.

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Image segmentation is one of the key factors in diagnosing glioma patients with brain tumors. It helps doctors identify the types of tumor that a patient is carrying and will lead to a prognosis that will help save the lives of patients. The analysis of medical images is a specialized domain in computer vision and image processing. This process extracts meaningful information from medical images that helps in treatment planning and monitoring the condition of patients. Deep learning models like CNN have shown promising results in image segmentation by identifying complex patterns in the image

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Hu, He-Xuan, Wen-Jie Mao, Zhen-Zhou Lin, Qiang Hu, and Ye Zhang. "Multimodal Brain Tumor Segmentation Based on an Intelligent UNET-LSTM Algorithm in Smart Hospitals." ACM Transactions on Internet Technology 21, no. 3 (2021): 1–14. http://dx.doi.org/10.1145/3450519.

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Smart hospitals are important components of smart cities. An intelligent medical system for brain tumor segmentation is required to construct smart hospitals. To achieve intelligent brain tumor segmentation, morphological variety and serious category imbalance must be managed effectively. Conventional deep neural networks have difficulty in predicting high-accuracy segmentation images due to these issues. To solve these problems, we propose using multimodal brain tumor images combined with the UNET and LSTM models to construct a new network structure with a mixed loss function to solve sample

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Liu, Dongwei, Ning Sheng, Tao He, Wei Wang, Jianxia Zhang, and Jianxin Zhang. "SGEResU-Net for brain tumor segmentation." Mathematical Biosciences and Engineering 19, no. 6 (2022): 5576–90. http://dx.doi.org/10.3934/mbe.2022261.

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<abstract><p>The precise segmentation of tumor regions plays a pivotal role in the diagnosis and treatment of brain tumors. However, due to the variable location, size, and shape of brain tumors, the automatic segmentation of brain tumors is a relatively challenging application. Recently, U-Net related methods, which largely improve the segmentation accuracy of brain tumors, have become the mainstream of this task. Following merits of the 3D U-Net architecture, this work constructs a novel 3D U-Net model called SGEResU-Net to segment brain tumors. SGEResU-Net simultaneously embeds

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Faisal Hafeez, Zobia Suhail, and Reyer Zwiggelaar. "Morphological and Marker-based Watershed Method for Detection and Segmentation of Brain Tumor Regions." NUST Journal of Engineering Sciences 16, no. 2 (2023): 108–13. http://dx.doi.org/10.24949/njes.v16i2.759.

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Brain tumor detection is a complex problem in medical image analysis. Brain tumor is an abnormal growth of brain cellsthat is usually detected by Magnetic Resonance Images (MRI). In this paper, we propose an efficient algorithm for detecting brain tumors using MRI without skull removal. After applying basic image preprocessing techniques, morphological operations are used to detect the boundaries and sharpen the regions.Segmentation is performed using Otsu thresholding and then a marker watershed technique is used for final brain tumor segmentation. The proposed approach is evaluated on 3000 i

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Zhang, Fuchun, Liang Wu, Yuwen Wang, et al. "A Multi-Scale Brain Tumor Segmentation Method based on U-Net Network." Journal of Physics: Conference Series 2289, no. 1 (2022): 012028. http://dx.doi.org/10.1088/1742-6596/2289/1/012028.

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Abstract Brain tumor is a serious threat to human health. Because the size and shape of brain tumors can be uneven, irregular and unstructured. Automatic segmentation of tumors from magnetic resonance imaging (MRI) is a challenging task. Brain tumor segmentation using computer-aided diagnosis has important clinical significance for the prognosis and treatment of patients. The traditional U-Net network can not take full advantage of context information, which is easy to cause the loss of effective information of image. Therefore, we propose a multi-scale segmentation method for brain tumors bas

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Syeda, Ateeq Fatima, and Asra Sarwath Prof. "Brain Tumor Detection Using Deep Learning." Journal of Scientific Research and Technology (JSRT) 1, no. 6 (2023): 256–64. https://doi.org/10.5281/zenodo.8373469.

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Brain tumor segmentation is an immensely challenging and crucial task, particularly when dealing with large datasets. The diversity in the appearance of brain tumors and their similarity to normal brain tissues makes the extraction of tumor regions from images exceptionally challenging. In our approach, we propose a method for extracting brain tumors from 2D Magnetic Resonance Brain Images (MRI) using the Fuzzy C-Means clustering algorithm, followed by the application of both traditional classifiers and Convolutional Neural Networks (CNN). Our comprehensive experimental study encompasses a rea

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Wu, Wentao, Daning Li, Jiaoyang Du, et al. "An Intelligent Diagnosis Method of Brain MRI Tumor Segmentation Using Deep Convolutional Neural Network and SVM Algorithm." Computational and Mathematical Methods in Medicine 2020 (July 14, 2020): 1–10. http://dx.doi.org/10.1155/2020/6789306.

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Among the currently proposed brain segmentation methods, brain tumor segmentation methods based on traditional image processing and machine learning are not ideal enough. Therefore, deep learning-based brain segmentation methods are widely used. In the brain tumor segmentation method based on deep learning, the convolutional network model has a good brain segmentation effect. The deep convolutional network model has the problems of a large number of parameters and large loss of information in the encoding and decoding process. This paper proposes a deep convolutional neural network fusion supp

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Nasrudin, Muhammad. "MRI-Based Brain Tumor Instance Segmentation Using Mask R-CNN." Computer Engineering and Applications Journal 13, no. 03 (2024): 1–9. http://dx.doi.org/10.18495/comengapp.v13i03.490.

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Brain tumor segmentation is a crucial step in medical image analysis for the accurate diagnosis and treatment of patients. Traditional methods for tumor segmentation often require extensive manual effort and are prone to variability. In this study, we propose an automated approach for brain tumor segmentation using Mask R-CNN, a state-of-the-art deep learning model for instance segmentation. Our method leverages MRI images to identify and delineate brain tumors with high precision. We trained the Mask R-CNN model on a dataset of annotated MRI images and evaluated its performance using the mean

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Iratni, Maya, Amira Abdullah, Mariam Aldhaheri, et al. "Transformers for Neuroimage Segmentation: Scoping Review." Journal of Medical Internet Research 27 (January 29, 2025): e57723. https://doi.org/10.2196/57723.

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Background Neuroimaging segmentation is increasingly important for diagnosing and planning treatments for neurological diseases. Manual segmentation is time-consuming, apart from being prone to human error and variability. Transformers are a promising deep learning approach for automated medical image segmentation. Objective This scoping review will synthesize current literature and assess the use of various transformer models for neuroimaging segmentation. Methods A systematic search in major databases, including Scopus, IEEE Xplore, PubMed, and ACM Digital Library, was carried out for studie

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Kazerooni, Anahita Fathi. "IMG-12. CBTN BRAIN TUMOR SEGMENTATION INITIATIVE: UPDATES ON MODEL RELEASE, HGG SEGMENTATION, AND SURVIVAL ANALYSIS." Neuro-Oncology 26, Supplement_4 (2024): 0. http://dx.doi.org/10.1093/neuonc/noae064.349.

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Abstract BACKGROUND Assessment of treatment responses in pediatric brain tumors requires accurate tumor segmentation, particularly important for nonresectable tumors like diffuse midline glioma (DMG), including diffuse intrinsic pontine glioma (DIPGs). Evaluating tumor progression in these tumors relies on monitoring tumor size changes in longitudinal MRI exams which is challenged by their infiltrative growth patterns. Our team developed a comprehensive multi-institutional, multi-histology autosegmentation deep learning (DL) model leveraging data from the Children’s Brain Tumor Network (CBTN),

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Gani, Timothy Abe, and Uten Emmoh Philemon. "A Deep Learning Hybridized Model for Segmentation of Medical Brain Tumors." International Journal of Novel Research in Computer Science and Software Engineering 11, no. 2 (2024): 25–44. https://doi.org/10.5281/zenodo.11574304.

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<strong>Abstract:</strong> This study conducted a comprehensive analysis of online brain tumor scan data, developing and evaluating a robust model to discern Intracranial Neoplasm brain tumors. Employing an empirical approach, we utilized Mask-RCNN and U-net for segmenting Intracranial Neoplasm tumors from brain Magnetic Resonance Images (MRIs). Our methodology encompassed dataset elucidation, data pre-processing, network architecture, training, testing strategies, and proposed ensemble methods. The dataset comprising 253 brain tumors and employed U-nets and Mask-RCNN, utilizing a statistical

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Wu, Mingliang, Hai-Li Ye, Yun Wu, and Jianmin Li. "Brain Tumor Image Segmentation Based on Grouped Convolution." Journal of Physics: Conference Series 2278, no. 1 (2022): 012042. http://dx.doi.org/10.1088/1742-6596/2278/1/012042.

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Abstract The automatic segmentation of MRI multi-modal images of brain tumors is one of the important research contents of disease detection and analysis. Due to the heterogeneity of tumors, it is difficult to achieve efficient and accurate automatic segmentation of brain tumors. Traditional segmentation methods based on machine learning cannot handle complex scenes such as complex edges and overlapping categories. In clinical assisted diagnosis, it is of great significance to apply deep learning to two-dimensional natural image segmentation and three-dimensional medical image segmentation. In

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Bhavani, Mrs R., and Dr K. Vasanth. "Classification of brain tumor using a multistage approach based on RELM and MLBP." EAI Endorsed Transactions on Pervasive Health and Technology 8, no. 4 (2023): e4. http://dx.doi.org/10.4108/eetpht.v8i4.3082.

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INTRODUCTION: Automatic segmentation and classification of brain tumors help in improvement of treatment which will increase the life of the patient. Tumor may be noncancerous (benign) or cancerous (malignant). Precancerous cells may also form into cancer.OBJECTIVES: Hough CNN is applied for selected section which applies hough casting technique in segmentation. METHODS: A multistage methodof extracting features, with multistage neighbouring is done for emerging an exact brain tumor classifying methodology.RESULTS: In this dataset three types of brain tumors are available they are meningioma,

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Wu, Wangxin, and Jian Zheng. "Research on Brain MRI Image Segmentation Based on Improved Unet." Advances in Engineering Technology Research 12, no. 1 (2024): 381. https://doi.org/10.56028/aetr.12.1.381.2024.

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Accurate segmentation of brain tumors plays an important role in assisting doctors in clinical diagnosis and treatment. A brain tumor segmentation algorithm based on an improved U-Net network is proposed to address the shortcomings of traditional U-Net networks in brain tumor segmentation accuracy. Propose to combine the U-Net model with ResNeXt50, using ResNeXt50 as the encoder part of U-net, utilizing its powerful feature extraction capability, and then passing these features to the decoder part of U-net for segmentation. At the same time, adding channel attention mechanism makes the model p

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K, R. Srivaishnavi, Pramananda Perumal T, and Anishiya P. "Brain Tumor Prediction and Segmentation with Morphological Region-based Active Contour Model and Refinement using Boltzmann Monte Carlo Method in MRI Images." Indian Journal of Science and Technology 17, no. 20 (2024): 2088–100. https://doi.org/10.17485/IJST/v17i20.1231.

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Abstract <strong>Objectives:</strong> The primary goal of the research work is to accurately detect the precise location of the brain tumor in the radiological Magnetic Resonance Imaging (MRI) images of human brain using segmentation method. <strong>Methods:</strong> In this research work, we introduce mainly the Morphological Region-based Active Contour model and Boltzmann Monte Carlo method (MACB model), involving a comprehensive three-step methodology for the segmentation of the brain, MRI images in order to detect brain tumor. The initial step involves pre-processing which i

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Deng, Yuchuan. "Head Tumor Segmentation and Detection Based on Resunet." Applied and Computational Engineering 99, no. 1 (2024): 89–94. http://dx.doi.org/10.54254/2755-2721/99/20251810.

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Deep learning algorithms are designed to mimic the functions of the human brain. These algorithms can simulate the human brain for feature extraction, i.e. neural networks with many hidden layers. This algorithm is being trained and tested using the MRI brain tumor segmentation dataset provided by Kaggle. ResNet50 is used for tumor classification tasks due to its powerful feature extraction capability. Through its deep residual structure, ResNet50 can effectively extract different lesion features and improve classification accuracy. At the same time, ResUNet combines the feature extraction cap

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Zhang, Ruifeng, Shasha Jia, Mohammed Jajere Adamuand, Weizhi Nie, Qiang Li, and Ting Wu. "HMNet: Hierarchical Multi-Scale Brain Tumor Segmentation Network." Journal of Clinical Medicine 12, no. 2 (2023): 538. http://dx.doi.org/10.3390/jcm12020538.

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An accurate and efficient automatic brain tumor segmentation algorithm is important for clinical practice. In recent years, there has been much interest in automatic segmentation algorithms that use convolutional neural networks. In this paper, we propose a novel hierarchical multi-scale segmentation network (HMNet), which contains a high-resolution branch and parallel multi-resolution branches. The high-resolution branch can keep track of the brain tumor’s spatial details, and the multi-resolution feature exchange and fusion allow the network’s receptive fields to adapt to brain tumors of dif

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Mukkapati, Naveen, and M. S. Anbarasi. "Brain Tumor Classification Based on Enhanced CNN Model." Revue d'Intelligence Artificielle 36, no. 1 (2022): 125–30. http://dx.doi.org/10.18280/ria.360114.

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Brain tumor classification is important process for doctors to plan the treatment for patients based on the stages. Various CNN based architecture is applied for the brain tumor classification to improve the classification performance. Existing methods in brain tumor segmentation have the limitations of overfitting and lower efficiency in handling large dataset. In this research, for brain tumor segmentation purpose the enhanced CNN architecture based on U-Net, for pattern analysis purpose RefineNet and for classifying brain tumor purpose SegNet architecture is proposed. The brain tumor benchm

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Sivamurugan, V., N. Radha, and R. Swathika. "Detection and segmentation of meningioma tumors using improved cloud empowered visual geometry group (cloud-ivgg) deep learning structure." Data and Metadata 4 (January 1, 2025): 478. https://doi.org/10.56294/dm2025478.

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Detection and segmentation of meningioma brain tumor is a complex process due to its similar textural pattern with other tumors. In this paper Meningioma Tumor Detection System (MTDS) approach is proposed to detect and classify the meningioma brain images from the healthy brain images. The training work flow of the proposed MTDS approach consists of Spatial Gabor Transform (SGT), feature computations and deep learning structure. The features are computed from the meningioma brain image dataset images and the normal brain image dataset images and these features are fed into the classification a

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Purohit, Nisha, and Chandi Prasad Bhatt. "Overview of Deep Learning Algorithms and Optimizers for Brain Tumor Segmentation." Journal of Medical Physics 50, no. 2 (2025): 185–97. https://doi.org/10.4103/jmp.jmp_12_25.

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Brain tumor segmentation is a vital process in medical imaging, essential for accurate diagnosis, treatment planning, and monitoring of brain tumors. Over the years, segmentation techniques have evolved from manual methods to machine learning approaches and, more recently, to deep learning techniques. The advent of deep learning, particularly convolutional neural networks, has revolutionized the field, allowing for end-to-end learning and eliminating the need for manual feature extraction. This review focuses on analyzing different deep learning architectures and explores their performance whe

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Chakravarthy R, Dr Arun. "Optimized Brain Tumor Detection Using Python Based Image Processing." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47854.

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Abstract—The manual delineation of brain neoplasms using magnetic resonance imaging (MRI) is complex, labor-intensive, and time-consuming. Accurate segmentation of brain tumors is critical for neuro-oncology diagnosis, radiation planning, and evaluating treatment response. Traditional automated segmentation methods rely on handcrafted feature extraction pipelines, which often lack generalizability. Moreover, standard deep learning approaches, especially convolutional neural networks (CNNs), require large annotated datasets for supervised learning—data that are scarce and costly in clinical neu

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Li, Haiyang, Xiaozhi Qi, Ying Hu, and Jianwei Zhang. "Arouse-Net: Enhancing Glioblastoma Segmentation in Multi-Parametric MRI with a Custom 3D Convolutional Neural Network and Attention Mechanism." Mathematics 13, no. 1 (2025): 160. https://doi.org/10.3390/math13010160.

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Glioblastoma, a highly aggressive brain tumor, is challenging to diagnose and treat due to its variable appearance and invasiveness. Traditional segmentation methods are often limited by inter-observer variability and the lack of annotated datasets. Addressing these challenges, this study introduces Arouse-Net, a 3D convolutional neural network that enhances feature extraction through dilated convolutions, improving tumor margin delineation. Our approach includes an attention mechanism to focus on edge features, essential for precise glioblastoma segmentation. The model’s performance is benchm

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Sørensen, Peter Jagd, Claes Nøhr Ladefoged, Vibeke Andrée Larsen, et al. "Repurposing the Public BraTS Dataset for Postoperative Brain Tumour Treatment Response Monitoring." Tomography 10, no. 9 (2024): 1397–410. http://dx.doi.org/10.3390/tomography10090105.

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The Brain Tumor Segmentation (BraTS) Challenge has been a main driver of the development of deep learning (DL) algorithms and provides by far the largest publicly available expert-annotated brain tumour dataset but contains solely preoperative examinations. The aim of our study was to facilitate the use of the BraTS dataset for training DL brain tumour segmentation algorithms for a postoperative setting. To this end, we introduced an automatic conversion of the three-label BraTS annotation protocol to a two-label annotation protocol suitable for postoperative brain tumour segmentation. To asse

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Bonato, Beatrice, Loris Nanni, and Alessandra Bertoldo. "Advancing Precision: A Comprehensive Review of MRI Segmentation Datasets from BraTS Challenges (2012–2025)." Sensors 25, no. 6 (2025): 1838. https://doi.org/10.3390/s25061838.

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Brain Tumor Segmentation (BraTS) challenges have significantly advanced research in brain tumor segmentation and related medical imaging tasks. This paper provides a comprehensive review of the BraTS datasets from 2012 to 2024, examining their evolution, challenges, and contributions to MRI-based brain tumor segmentation. Over the years, the datasets have grown in size, complexity, and scope, incorporating refined pre-processing and annotation protocols. By synthesizing insights from over a decade of BraTS challenges, this review elucidates the progression of dataset curation, highlights the i

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Atiyah, Assalah, and Khawla Ali. "Brain MRI Images Segmentation Based on U-Net Architecture." Iraqi Journal for Electrical and Electronic Engineering 18, no. 1 (2021): 21–27. http://dx.doi.org/10.37917/ijeee.18.1.3.

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Brain tumors are collections of abnormal tissues within the brain. The regular function of the brain may be affected as it grows within the region of the skull. Brain tumors are critical for improving treatment options and patient survival rates to prevent and treat them. The diagnosis of cancer utilizing manual approaches for numerous magnetic resonance imaging (MRI) images is the most complex and time-consuming task. Brain tumor segmentation must be carried out automatically. A proposed strategy for brain tumor segmentation is developed in this paper. For this purpose, images are segmented b

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Tahon, Nourel hoda, Nader Ashraf, Ahmed Moawad, et al. "DSAI-05 THE BRAIN TUMOR SEGMENTATION (BRATS-METS) CHALLENGE 2023: BRAIN METASTASIS SEGMENTATION ON PRE-TREATMENT MRI." Neuro-Oncology Advances 6, Supplement_1 (2024): i12. http://dx.doi.org/10.1093/noajnl/vdae090.037.

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Abstract PURPOSE Clinical monitoring of metastatic disease to the brain using magnetic resonance imaging (MRI) can be laborious and time-consuming, particularly when multiple small metastases are involved and assessments are performed manually. METHODS AND MATERIALS The BraTS-METS 2023 dataset is acquired from varying MRI imaging quality across different vendors. The scans are pre-processed using different algorithms refined by a pool of annotators with different expertise. Two independent board-certified neuroradiologists finally reviewed the dataset. The datasets are divided into Training, v

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Gull, Sahar, Shahzad Akbar, and Habib Ullah Khan. "Automated Detection of Brain Tumor through Magnetic Resonance Images Using Convolutional Neural Network." BioMed Research International 2021 (November 30, 2021): 1–14. http://dx.doi.org/10.1155/2021/3365043.

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Brain tumor is a fatal disease, caused by the growth of abnormal cells in the brain tissues. Therefore, early and accurate detection of this disease can save patient’s life. This paper proposes a novel framework for the detection of brain tumor using magnetic resonance (MR) images. The framework is based on the fully convolutional neural network (FCNN) and transfer learning techniques. The proposed framework has five stages which are preprocessing, skull stripping, CNN-based tumor segmentation, postprocessing, and transfer learning-based brain tumor binary classification. In preprocessing, the

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Rehman, Azka, Muhammad Usman, Abdullah Shahid, Siddique Latif, and Junaid Qadir. "Selective Deeply Supervised Multi-Scale Attention Network for Brain Tumor Segmentation." Sensors 23, no. 4 (2023): 2346. http://dx.doi.org/10.3390/s23042346.

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Brain tumors are among the deadliest forms of cancer, characterized by abnormal proliferation of brain cells. While early identification of brain tumors can greatly aid in their therapy, the process of manual segmentation performed by expert doctors, which is often time-consuming, tedious, and prone to human error, can act as a bottleneck in the diagnostic process. This motivates the development of automated algorithms for brain tumor segmentation. However, accurately segmenting the enhanced and core tumor regions is complicated due to high levels of inter- and intra-tumor heterogeneity in ter

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Vinoth Kumar, V., and B. Paulchamy. "Tumor Categorization Model (TCM) Using Soft Computing Techniques for Providing Efficient Medical Support in Brain Tumor Treatments." Journal of Medical Imaging and Health Informatics 11, no. 11 (2021): 2806–13. http://dx.doi.org/10.1166/jmihi.2021.3872.

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Brain cancer identification and segmentation is a prolonged and difficult task in Medical Image Processing, which is most significant for providing appropriate treatment and increase patient’s life span. With the advancements available in medical fields, soft computing techniques are incorporated to accurate detection and classification of brain tumors. Besides brain cancer detection, it is vital to categorize tumor stage based on their features. For that concern, this paper develops a Tumor Categorization Model (TCM) that includes image processing and soft computing techniques. Here, pre-proc

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Sagiroglu, Seref, Ramazan Terzi, Emrah Celtikci, et al. "A novel brain tumor magnetic resonance imaging dataset (Gazi Brains 2020): initial benchmark results and comprehensive analysis." PeerJ Computer Science 11 (June 10, 2025): e2920. https://doi.org/10.7717/peerj-cs.2920.

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This article presents a new benchmark MRI dataset called the Gazi Brains Dataset 2020, containing MRI images of 100 patients, and introduces initial experimental results performed on this dataset in comparison with available brain MRI datasets. Furthermore, the dataset is analyzed using eight different deep learning models for high-grade glioma tumor prediction, classification, and detection tasks. Additionally, this study demonstrates the results of an explainable Artificial Intelligence (XAI) approach applied to the trained models. To demonstrate the utility of the proposed dataset, differen

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Aggarwal, Mukul, Amod Kumar Tiwari, and M. Partha Sarathi. "Comparative Analysis of Deep Learning Models on Brain Tumor Segmentation Datasets: BraTS 2015-2020 Datasets." Revue d'Intelligence Artificielle 36, no. 6 (2022): 863–71. http://dx.doi.org/10.18280/ria.360606.

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Deep Learning neural networks have shown applicability in segmentation of brain tumor images.This research have been carried for comprehensive review of several deep learning neural networks. The datasets included in this study are standard datasets Multimodal Brain Tumor Segmentation (BraTS). This paper has summarized the performance of various deep learning neural network algorithms on BraTS datasets. Algorithms have been compared and summarized against the baseline models with specific attributes like dice score, PPV and sensitivity. It has been found that out of the different models applie

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Dalal, Surjeet, Umesh Kumar Lilhore, Poongodi Manoharan, et al. "An Efficient Brain Tumor Segmentation Method Based on Adaptive Moving Self-Organizing Map and Fuzzy K-Mean Clustering." Sensors 23, no. 18 (2023): 7816. http://dx.doi.org/10.3390/s23187816.

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Brain tumors in Magnetic resonance image segmentation is challenging research. With the advent of a new era and research into machine learning, tumor detection and segmentation generated significant interest in the research world. This research presents an efficient tumor detection and segmentation technique using an adaptive moving self-organizing map and Fuzzyk-mean clustering (AMSOM-FKM). The proposed method mainly focused on tumor segmentation using extraction of the tumor region. AMSOM is an artificial neural technique whose training is unsupervised. This research utilized the online Kagg

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Duman, Abdulkerim, Oktay Karakuş, Xianfang Sun, Solly Thomas, James Powell, and Emiliano Spezi. "RFS+: A Clinically Adaptable and Computationally Efficient Strategy for Enhanced Brain Tumor Segmentation." Cancers 15, no. 23 (2023): 5620. http://dx.doi.org/10.3390/cancers15235620.

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Automated brain tumor segmentation has significant importance, especially for disease diagnosis and treatment planning. The study utilizes a range of MRI modalities, namely T1-weighted (T1), T1-contrast-enhanced (T1ce), T2-weighted (T2), and fluid-attenuated inversion recovery (FLAIR), with each providing unique and vital information for accurate tumor localization. While state-of-the-art models perform well on standardized datasets like the BraTS dataset, their suitability in diverse clinical settings (matrix size, slice thickness, manufacturer-related differences such as repetition time, and

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