ArticleBMC medical informatics and decision making2023
An early detection and segmentation of Brain Tumor using Deep Neural Network.
Article in BMC medical informatics and decision making, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 121 citations in OpenAlex.
- A hybrid deep learning framework based on VGG19 and U-Net for accurate brain tumor segmentation in MRI images.Magnetic resonance letters · 2026Article
- High-precision brain tumor segmentation with switchable normalization in faster R-CNN architecture.Scientific reports · 2026Article
- MRI-based brain tumor prediction using convolutional neural network framework.Scientific reports · 2026Article
- SPiKe-Med: a hybrid spiking-CNN-transformer framework for energy-efficient brain tumor MRI segmentation.Frontiers in artificial intelligence · 2026Article
- Boosting brain tumor detection with an optimized ResNet and explainability via Grad-CAM and LIME.Brain informatics · 2025Article
- Deep Learning-Based Assessment of Brainstem Volume Changes in Spinocerebellar Ataxia Type 2 (SCA2): A Study on Patients and Preclinical Subjects.Sensors (Basel, Switzerland) · 2025Article
- A brain tumor segmentation enhancement in MRI images using U-Net and transfer learning.BMC medical imaging · 2025Article
- CLIF-Net: Intersection-guided Cross-view Fusion Network for Infection Detection from Cranial Ultrasound.medRxiv : the preprint server for health sciences · 2025Article
- Feasibility Study of Detecting and Segmenting Small Brain Tumors in a Small MRI Dataset with Self-Supervised Learning.Diagnostics (Basel, Switzerland) · 2025Article
- Lightweight-CancerNet: a deep learning approach for brain tumor detection.PeerJ. Computer science · 2025Article
- VISION: View-specific integrated segmentation-classification framework for accurate brain tumor detection in MRI scans.PloS one · 2025Article
- Dynamic Focus on Tumor Boundaries: A Lightweight U-Net for MRI Brain Tumor Segmentation.Bioengineering (Basel, Switzerland) · 2024Article
- Brain tumor diagnosis in MRI scans images using Residual/Shuffle Network optimized by augmented Falcon Finch optimization.Scientific reports · 2024Article
- End-to-End Multi-task Learning Architecture for Brain Tumor Analysis with Uncertainty Estimation in MRI Images.Journal of imaging informatics in medicine · 2024Article
- Enhancing brain tumor detection in MRI images through explainable AI using Grad-CAM with Resnet 50.BMC medical imaging · 2024Article
- Enhancing brain tumor classification in MRI scans with a multi-layer customized convolutional neural network approach.Frontiers in computational neuroscience · 2024Article
- Advancing Brain Tumor Classification through Fine-Tuned Vision Transformers: A Comparative Study of Pre-Trained Models.Sensors (Basel, Switzerland) · 2023Article
- Survival and grade of the glioma prediction using transfer learning.PeerJ. Computer science · 2023Article
- Automated pediatric brain tumor imaging assessment tool from CBTN: Enhancing suprasellar region inclusion and managing limited data with deep learning.Neuro-oncology advancesArticle
- A systematic review of deep learning-based segmentation techniques for brain tumor detection (2013-2023).Digital healthReview
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMagnetic resonance image (MRI) brain tumor segmentation is crucial and important in the medical field, which can help in diagnosis and prognosis, overall growth predictions, Tumor density measures, and care plans needed for patients. The difficulty in segmenting brain Tumors is primarily because of the wide range of structures, shapes, frequency, position, and visual appeal of Tumors, like intensity, contrast, and visual variation. With recent advancements in Deep Neural Networks (DNN) for image classification tasks, intelligent medical image segmentation is an exciting direction for Brain Tumor research. DNN requires a lot of time & processing capabilities to train because of only some gradient diffusion difficulty and its complication.
methodsTo overcome the gradient issue of DNN, this research work provides an efficient method for brain Tumor segmentation based on the Improved Residual Network (ResNet). Existing ResNet can be improved by maintaining the details of all the available connection links or by improving projection shortcuts. These details are fed to later phases, due to which improved ResNet achieves higher precision and can speed up the learning process.
resultsThe proposed improved Resnet address all three main components of existing ResNet: the flow of information through the network layers, the residual building block, and the projection shortcut. This approach minimizes computational costs and speeds up the process.
conclusionAn experimental analysis of the BRATS 2020 MRI sample data reveals that the proposed methodology achieves competitive performance over the traditional methods like CNN and Fully Convolution Neural Network (FCN) in more than 10% improved accuracy, recall, and f-measure.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.