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BT-Net: An end-to-end multi-task architecture for brain tumor classification, segmentation, and localization from MRI images

https://doi.org/10.1016/j.array.2024.100346

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1 of the 34 references listed on this page is in the Retraction Watch database as retracted (export 2026-11-11): 10.1007/s00500-022-07163-z (notice 2024-10-28).

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-319-24574-4_28openalex-snapshot

U-Net: Convolutional Networks for Biomedical Image Segmentation

inferredcites →https://doi.org/10.1007/s00500-022-07163-zopenalex-snapshot

RETRACTED ARTICLE: Brain tumor magnetic resonance image classification: a deep learning approach

inferredcites →https://doi.org/10.1007/s11042-020-10335-4openalex-snapshot

Multi-class brain tumor classification using residual network and global average pooling

inferredcites →https://doi.org/10.1007/s11042-020-10443-1openalex-snapshot

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

inferredcites →https://doi.org/10.1007/s11042-022-12162-1openalex-snapshot

RETRACTED ARTICLE: Review of brain tumor detection from MRI images with hybrid approaches

inferredcites →https://doi.org/10.1007/s11063-020-10398-2openalex-snapshot

Deep CNN for Brain Tumor Classification

inferredcites →https://doi.org/10.1007/s11910-013-0347-2openalex-snapshot

Response Assessment Criteria for Glioblastoma: Practical Adaptation and Implementation in Clinical Trials of Antiangiogenic Therapy

inferredcites →https://doi.org/10.1007/s40747-021-00563-yopenalex-snapshot

Brain tumor detection and classification using machine learning: a comprehensive survey

inferredcites →https://doi.org/10.1016/j.array.2019.100004openalex-snapshot

A review: Deep learning for medical image segmentation using multi-modality fusion

inferredcites →https://doi.org/10.1016/j.array.2022.100256openalex-snapshot

BreastMultiNet: A Multi-Scale Feature Fusion Method Using Deep Neural Network to Detect Breast Cancer

inferredcites →https://doi.org/10.1016/j.isprsjprs.2020.01.013openalex-snapshot

ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data

inferredcites →https://doi.org/10.1016/j.jrras.2022.05.014openalex-snapshot

MR image normalization dilemma and the accuracy of brain tumor classification model

inferredcites →https://doi.org/10.1016/j.patcog.2020.107404openalex-snapshot

U2-Net: Going deeper with nested U-structure for salient object detection

inferredcites →https://doi.org/10.1016/j.sysarc.2019.02.008openalex-snapshot

Filter-based deep-compression with global average pooling for convolutional networks

inferredcites →https://doi.org/10.1049/ipr2.12619openalex-snapshot

Brain tumour detection in magnetic resonance imaging using Levenberg–Marquardt backpropagation neural network

inferredcites →https://doi.org/10.1109/access.2021.3086020openalex-snapshot

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

inferredcites →https://doi.org/10.1109/access.2022.3153108openalex-snapshot

Brain Tumor and Glioma Grade Classification Using Gaussian Convolutional Neural Network

inferredcites →https://doi.org/10.1109/cvpr.2016.308openalex-snapshot

Rethinking the Inception Architecture for Computer Vision

inferredcites →https://doi.org/10.1109/gmag.2003.1219671openalex-snapshot

Geometric design and space planning using the marching squares and marching cube algorithms

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The Dural Tail Sign

inferredcites →https://doi.org/10.1148/radiology.170.3.2916043openalex-snapshot

Meningiomas: MR and histopathologic features.

inferredcites →https://doi.org/10.1155/2022/4928096openalex-snapshot

Framework to Segment and Evaluate Multiple Sclerosis Lesion in MRI Slices Using VGG-UNet

inferredcites →https://doi.org/10.1158/1078-0432.ccr-12-3002openalex-snapshot

The Definition of Primary and Secondary Glioblastoma

inferredcites →https://doi.org/10.1586/14737175.8.4.563openalex-snapshot

Pituitary tumors in childhood: update of diagnosis, treatment and molecular genetics

inferredcites →https://doi.org/10.1609/aaai.v31i1.11231openalex-snapshot

Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

inferredcites →https://doi.org/10.3322/caac.21763openalex-snapshot

Cancer statistics, 2023

inferredcites →https://doi.org/10.3389/fgene.2022.822666openalex-snapshot

Explanation-Driven Deep Learning Model for Prediction of Brain Tumour Status Using MRI Image Data

inferredcites →https://doi.org/10.3389/fnins.2021.679847openalex-snapshot

Brain Tumor MR Image Classification Using Convolutional Dictionary Learning With Local Constraint

inferredcites →https://doi.org/10.3390/app10061999openalex-snapshot

Classification of Brain Tumors from MRI Images Using a Convolutional Neural Network

inferredcites →https://doi.org/10.3390/brainsci12091145openalex-snapshot

RMTF-Net: Residual Mix Transformer Fusion Net for 2D Brain Tumor Segmentation

inferredcites →https://doi.org/10.3390/life13020349openalex-snapshot

Multiple Brain Tumor Classification with Dense CNN Architecture Using Brain MRI Images

inferredcites →https://doi.org/10.48550/arxiv.1412.6980openalex-snapshot

Adam: A Method for Stochastic Optimization

inferredcites →https://doi.org/10.48550/arxiv.1804.03999openalex-snapshot

Attention U-Net: Learning Where to Look for the Pancreas

inferredcites →https://doi.org/10.48550/arxiv.2102.04306openalex-snapshot

TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Organ-Specific Cancer Diagnosis: A Machine Learning-Based GUI Approach

inferred← citeshttps://doi.org/10.1007/978-3-032-18282-1_15openalex-snapshot

Comparative Analysis Brain Tumor Classification Using Different Convolutional Neural Networks (CNN) Algorithms

inferred← citeshttps://doi.org/10.1007/978-3-032-20722-7_1openalex-snapshot

Performance Comparison of Machine Learning Models for Brain Tumor Classification Using Feature-Extracted MRI and CT Images

inferred← citeshttps://doi.org/10.1007/978-981-96-8309-3_25openalex-snapshot

Brain Tumor Classification of MRI Data Using Deep Semi-transfer Learning Framework

inferred← citeshttps://doi.org/10.1007/s00521-025-11216-3openalex-snapshot

Efficient and accurate brain tumor detection and classification using advanced hybrid filtering and self-attention generative adversarial networks

inferred← citeshttps://doi.org/10.1007/s11042-025-21034-3openalex-snapshot

An efficient deep learning framework for detecting and classifying brain tumour from DWT compressed MRI images

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Fully automatic brain tumor segmentation and classification on synthetically generated 3D magnetic resonance images via 3D trans dilated mobileUNet and adaptive mobilenetV2

inferred← citeshttps://doi.org/10.1007/s42979-025-03894-6openalex-snapshot

An Intelligent Medical Model for Classification of Brain Tumours and Stroke Lesions Using Machine Learning in Healthcare for Resource-Constrained Devices

inferred← citeshttps://doi.org/10.1007/s42979-026-05013-5openalex-snapshot

An Effective Brain Tumour Segmentation and Classification System using Optimized YOLO V9 and Hybrid Deep Transfer Learning Model

inferred← citeshttps://doi.org/10.1007/s44443-026-00973-3openalex-snapshot

CrossTaskFormer: Bidirectional cross-task attention for joint brain tumor segmentation and classification

inferred← citeshttps://doi.org/10.1016/j.array.2025.100376openalex-snapshot

An attention based residual U-Net with swin transformer for brain MRI segmentation

inferred← citeshttps://doi.org/10.1016/j.array.2025.100446openalex-snapshot

CLORG: A contrastive learning-based framework for morphological representation and classification of organoids

inferred← citeshttps://doi.org/10.1016/j.array.2025.100468openalex-snapshot

NEDL-GCP: A nested ensemble deep learning model for Gynecological cancer risk prediction

inferred← citeshttps://doi.org/10.1016/j.bspc.2025.107560openalex-snapshot

MRI intracranial Neoplasm classification using hybrid LOA-based deep learning classifier

inferred← citeshttps://doi.org/10.1016/j.bspc.2025.109192openalex-snapshot

HBTF-Net: A hierarchical Bayesian multi-task fusion network for joint classification and segmentation of cervical cells

inferred← citeshttps://doi.org/10.1016/j.bspc.2026.110024openalex-snapshot

TransEdgeNet: a self-supervised hybrid network with token pruning and temporal-spatial attention for real-time medical image segmentation

inferred← citeshttps://doi.org/10.1016/j.bspc.2026.110029openalex-snapshot

Multi-attention-based brain tumor classification network: an advanced framework for accurate diagnosis across multiple imaging modalities

inferred← citeshttps://doi.org/10.1016/j.bspc.2026.110446openalex-snapshot

Hybrid deep learning network enabled brain tumor detection with shape-aware loss-based structure correcting adversarial network using MRI

inferred← citeshttps://doi.org/10.1016/j.ibmed.2026.100361openalex-snapshot

Dual-stage deep learning framework for brain tumor classification and localization using multimodal MRI scans

inferred← citeshttps://doi.org/10.1016/j.jneumeth.2026.110724openalex-snapshot

An optimized hierarchical attention assisted deep learning model for brain tissue classification

inferred← citeshttps://doi.org/10.1016/j.neucom.2025.130996openalex-snapshot

SMT-DL: A semi-supervised multi-task learning framework based on dictionary learning for robust feature sharing

inferred← citeshttps://doi.org/10.1016/j.rineng.2024.102994openalex-snapshot

Enhanced TumorNet: Leveraging YOLOv8s and U-net for superior brain tumor detection and segmentation utilizing MRI scans

inferred← citeshttps://doi.org/10.1038/s41598-025-92776-1openalex-snapshot

Automated multi-class MRI brain tumor classification and segmentation using deformable attention and saliency mapping

inferred← citeshttps://doi.org/10.1049/aie2.70009openalex-snapshot

Multi‐Modal Deep Feature Extraction and Classifier‐Level Integration for Brain Tumour Classification Using CT and MRI Image

inferred← citeshttps://doi.org/10.1109/access.2025.3554405openalex-snapshot

Deep Learning-Based MRI Brain Tumor Segmentation With EfficientNet-Enhanced UNet

inferred← citeshttps://doi.org/10.1109/cai64502.2025.00100openalex-snapshot

Brain Tumour Diagnostics and Analysis (TDA): Segmentation, Classification and Interactive Interface

inferred← citeshttps://doi.org/10.1109/iccda64887.2024.10867268openalex-snapshot

A Novel Technique for Brain Tumour Detection Using IMADO

inferred← citeshttps://doi.org/10.1109/iccit68739.2025.11491424openalex-snapshot

AG3D-U 2 Net: Attention Gated 3D U 2 -Net for Automated Brain Tumor Segmentation in 3D MRI

inferred← citeshttps://doi.org/10.1109/icirca65293.2025.11089719openalex-snapshot

Improved Median Filtering with LeNet Model for Brain Tumor Classification

inferred← citeshttps://doi.org/10.1109/icmnwc66779.2025.11354391openalex-snapshot

AAG-NAS-MSCNet: Enhanced Nested Transformer U-Net with Neural Architecture Search and Multi-Scale Classification Network for Brain Tumor Segmentation and Classification

inferred← citeshttps://doi.org/10.1109/icriset64803.2025.11252277openalex-snapshot

Hybrid Multilevel Thresholding for Skull Base Tumor Image Segmentation and Classification Using Improved YOLOv8s Model

inferred← citeshttps://doi.org/10.1109/iraset68627.2026.11538624openalex-snapshot

Comparative Study of Deep Learning Architectures for Brain Tumor Segmentation

inferred← citeshttps://doi.org/10.1109/slaai-icai68534.2025.11318534openalex-snapshot

Multi-Task Learning Framework for Brain Tumor Segmentation and Classification

inferred← citeshttps://doi.org/10.2174/0118749445421967251007105043openalex-snapshot

Self-Prompting Hybrid YOLOv12-SAM 2 Model for MRI Brain Tumour Segmentation in Real Time

inferred← citeshttps://doi.org/10.33022/ijcs.v13i3.4028openalex-snapshot

Deep and Machine Learning Algorithms for Diagnosing Brain Cancer and Tumors

inferred← citeshttps://doi.org/10.3389/fmed.2026.1955863openalex-snapshot

Near-duplicate contamination inflates brain MRI benchmark performance: a size-matched, multi-seed MedNeXt-B study

inferred← citeshttps://doi.org/10.3389/fnins.2026.1875642openalex-snapshot

Convolutional neural networks in brain disease diagnosis: a unified review of Alzheimer's, Parkinson's, and brain tumor classification

inferred← citeshttps://doi.org/10.3390/brainsci16010037openalex-snapshot

A Multi-Task EfficientNetV2S Approach with Hierarchical Hybrid Attention for MRI Enhancing Brain Tumor Segmentation and Classification

inferred← citeshttps://doi.org/10.3390/diagnostics15182326openalex-snapshot

Automated Brain Tumor MRI Segmentation Using ARU-Net with Residual-Attention Modules

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