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Field: Brain Tumor Detection and Classification

MultiResUNet : Rethinking the U-Net architecture for multimodal biomedical image segmentation

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Nabil Ibtehaz, M. Sohel Rahman

Journal: Neural Networks
Year: 2019
Citations: 2251

In recent years Deep Learning has brought about a breakthrough in Medical Image Segmentation. In this regard, U-Net has been the most popular architecture in the medical imaging community. Despite outstanding overall performance in segmenting multimodal medical images, through extensive experimentat...

Physical SciencesComputer ScienceArtificial IntelligenceOpen Access
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CoroDet: A deep learning based classification for COVID-19 detection using chest X-ray images

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Emtiaz Hussain, Mahmudul Hasan, Anisur Rahman, Ickjai Lee et al.

Journal: Chaos Solitons & FractalsYear: 2020Citations: 454

Highlights • A 22-layer CNN architecture, which has achieved an accuracy of 99.1% for 2 class classification, 94.2% for 3 class classification, and 91.2% for 4 class classification. To the best of our knowledge, the accuracy of our proposed CoroDet method is higher than the state-of-the-art method f...

Health SciencesMedicineRadiology, Nuclear Medicine and ImagingOpen Access
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Accurate brain tumor detection using deep convolutional neural network

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Md. Saikat Islam Khan, Anichur Rahman, Tanoy Debnath, Md. Razaul Karim et al.

Journal: Computational and Structural Biotechnology JournalYear: 2022Citations: 380

Detection and Classification of a brain tumor is an important step to better understanding its mechanism. Magnetic Reasoning Imaging (MRI) is an experimental medical imaging technique that helps the radiologist find the tumor region. However, it is a time taking process and requires expertise to tes...

Life SciencesNeuroscienceNeurologyOpen Access
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Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia

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Manan Binth Taj Noor, Nusrat Zerin Zenia, M. Shamim Kaiser, Shamim Al Mamun et al.

Journal: Brain InformaticsYear: 2020Citations: 335

Neuroimaging, in particular magnetic resonance imaging (MRI), has been playing an important role in understanding brain functionalities and its disorders during the last couple of decades. These cutting-edge MRI scans, supported by high-performance computational tools and novel ML techniques, have o...

Life SciencesNeuroscienceNeurologyOpen Access
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Brain Tumor Detection Using Convolutional Neural Network

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Tonmoy Hossain, Fairuz Shadmani Shishir, Mohsena Ashraf, MD Abdullah Al Nasim et al.

Journal: 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT)Year: 2019Citations: 312

Brain Tumor segmentation is one of the most crucial and arduous tasks in the terrain of medical image processing as a human-assisted manual classification can result in inaccurate prediction and diagnosis. Moreover, it is an aggravating task when there is a large amount of data present to be assiste...

Life SciencesNeuroscienceNeurology
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Deep learning for medical image segmentation: State-of-the-art advancements and challenges

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Md. Eshmam Rayed, S. M. Sajibul Islam, Sadia Islam Niha, Jamin Rahman Jim et al.

Journal: Informatics in Medicine UnlockedYear: 2024Citations: 277

Image segmentation, a crucial process of dividing images into distinct parts or objects, has witnessed remarkable advancements with the emergence of deep learning (DL) techniques. The use of layers in deep neural networks, like object form recognition in higher layers and basic edge identification i...

Physical SciencesComputer ScienceComputer Vision and Pattern RecognitionOpen Access
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Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances

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Protima Khan, Md. Fazlul Kader, S. M. Riazul Islam, Aisha B Rahman et al.

Journal: IEEE AccessYear: 2021Citations: 264

Brain is the controlling center of our body. With the advent of time, newer and newer brain diseases are being discovered. Thus, because of the variability of brain diseases, existing diagnosis or detection systems are becoming challenging and are still an open problem for research. Detection of bra...

Life SciencesNeuroscienceNeurologyOpen Access
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VGG-SCNet: A VGG Net-Based Deep Learning Framework for Brain Tumor Detection on MRI Images

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Mohammad Shahjahan Majib, Md. Mahbubur Rahman, T. M. Shahriar Sazzad, Nafiz Imtiaz Khan et al.

Journal: IEEE AccessYear: 2021Citations: 260

A brain tumor is a life-threatening neurological condition caused by the unregulated development of cells inside the brain or skull. The death rate of people with this condition is steadily increasing. Early diagnosis of malignant tumors is critical for providing treatment to patients, and early dis...

Life SciencesNeuroscienceNeurologyOpen Access
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Robust clinical applicable CNN and U-Net based algorithm for MRI classification and segmentation for brain tumor

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Atika Akter, Nazeela Nosheen, Sabbir Ahmed, Mariom Hossain et al.

Journal: Expert Systems with ApplicationsYear: 2023Citations: 242

Early diagnosis of brain tumors is critical for enhancing patient prognosis and treatment options, while accurate classification and segmentation of brain tumors are vital for developing personalized treatment strategies. Despite the widespread use of Magnetic Resonance Imaging (MRI) for brain exami...

Life SciencesNeuroscienceNeurologyOpen Access
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Brain Tumor Classification Using Convolutional Neural Network

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Sunanda Das, O. F. M. Riaz Rahman Aranya, Nishat Nayla Labiba

Journal: 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT)Year: 2019Citations: 215

Medical image classification has gained tremendous attention in recent years, and Convolutional Neural Network (CNN) is the most widespread neural network model for image classification problem. CNN is designed to determine features adaptively through backpropagation by applying numerous building bl...

Life SciencesNeuroscienceNeurology
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Vision Transformers, Ensemble Model, and Transfer Learning Leveraging Explainable AI for Brain Tumor Detection and Classification

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Shahriar Hossain, Amitabha Chakrabarty, Thippa Reddy Gadekallu, Mamoun Alazab et al.

Journal: IEEE Journal of Biomedical and Health InformaticsYear: 2023Citations: 206

The abnormal growth of malignant or nonmalignant tissues in the brain causes long-term damage to the brain. Magnetic resonance imaging (MRI) is one of the most common methods of detecting brain tumors. To determine whether a patient has a brain tumor, MRI filters are physically examined by experts a...

Life SciencesNeuroscienceNeurology
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Diagnosis of breast cancer based on modern mammography using hybrid transfer learning

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Aditya Khamparia, Subrato Bharati, Prajoy Podder, Deepak Gupta et al.

Journal: Multidimensional Systems and Signal ProcessingYear: 2021Citations: 199

Breast cancer is a common cancer in women. Early detection of breast cancer in particular and cancer, in general, can considerably increase the survival rate of women, and it can be much more effective. This paper mainly focuses on the transfer learning process to detect breast cancer. Modified VGG ...

Physical SciencesComputer ScienceArtificial IntelligenceOpen Access
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Stroke Disease Detection and Prediction Using Robust Learning Approaches

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Tahia Tazin, Md Nur Alam, Nahian Nakiba Dola, Mohammad Sajibul Bari et al.

Journal: Journal of Healthcare EngineeringYear: 2021Citations: 198

Stroke is a medical disorder in which the blood arteries in the brain are ruptured, causing damage to the brain. When the supply of blood and other nutrients to the brain is interrupted, symptoms might develop. According to the World Health Organization (WHO), stroke is the greatest cause of death a...

Health SciencesHealth ProfessionsHealth Information ManagementOpen Access
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MRI brain tumor detection and classification using parallel deep convolutional neural networks

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Takowa Rahman, Md. Saiful Islam

Journal: Measurement SensorsYear: 2023Citations: 197

Convolutional neural network (CNN) is widely used to classify brain tumors with high accuracy. Since CNN collects features randomly without knowing the local and global features and causes overfitting problems, this research proposes a novel parallel deep convolutional neural network (PDCNN) topolog...

Life SciencesNeuroscienceNeurologyOpen Access
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Performance Analysis of Machine Learning Approaches in Stroke Prediction

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Minhaz Uddin Emon, Maria Sultana Keya, Tamara Islam Meghla, Md. Mahfujur Rahman et al.

Journal: 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)Year: 2020Citations: 194

Most of strokes will occur due to an unexpected obstruction of courses by prompting both the brain and heart. Early awareness for different warning signs of stroke can minimize the stroke. This research work proposes an early prediction of stroke diseases by using different machine learning approach...

Health SciencesHealth ProfessionsHealth Information Management
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