Brain Tumor Classification

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3 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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Scores That Hold, Benchmarks That Leak: Measuring Dataset Contamination in Public Brain-Tumor MRI Classification
Sep 30, 2026Bhanu Prakash Vangala, Sowmya Guda, Latha Peddi +1Data Leakage Benchmark Contamination
Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211 USA · Government Degree College (Autonomous), Siddipet, Telangana 502103, India · Amar Biotech Private Limited, Kondapur, Hyderabad, Telangana 500084, India
Towards Trustworthy AI for Glioma Diagnosis: A Task-Aware Evaluation of Uncertainty Quantification
Sep 30, 2026Gonzalo Esteban Mosquera Rojas, Sebastian R. van der Voort, Carolin M. Pirkl +3Uncertainty Quantification Brain Tumor Classification
Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands · Department of Medical Informatics, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands · GE HealthCare, Munich, Germany +3
A Vision-Language Foundation Model for Precise and Comprehensive Brain Tumor Diagnosis from Preoperative Multimodal Data
Sep 15, 2026Yinong Wang, Jianwen Chen, Zhou Chen +28Medical VLMs Brain Tumor Classification
Joyce · School of Computing and Data Science, The University of Hong Kong, China · Department of Neurosurgery, Xiangya Hospital, Central South University, China +23
The Diagnosis a Reporter Leaves Unspoken: Surfacing Frozen Tumor Features for Brain-Tumor MRI Reporting
Sep 2, 2026Khawaja Murad ul Hassan, Ruqiyya Adil, Adil Qayyum +4Radiology Report Generation Brain MRI
National University of Sciences and Technology, Islamabad, Pakistan · Consultant Radiologist, Rawalpindi, Pakistan · Bahria University Health Sciences Campus, Islamabad, Pakistan +1
NeuroMosaic: Anatomically Grounded Multimodal Large Language Modeling for Molecularly Aware Glioma Reasoning from 3D MRI and Clinical Narratives
Aug 4, 2026Yantong Liu, Zheyu Zhang, Runpeng Liu +3Multimodal Large Language Models Medical VLMs
Department of Computer Information Engineering, Kunsan National University, Gunsan 54150, Republic of Korea · School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China
DS@GT ARC at MEDIQA-CORE-Task-1 2026: Trimodal Model Fusion with Task-Specific Gates for Brain Tumor Subtype Classification
Jul 30, 2026Hoang Thanh Thanh Truong, Charles R. ClarkMultimodal Classification Brain Tumor Classification
Georgia Institute of Technology, North Ave NW, Atlanta, GA 30332 · University of Florida, Stadium Rd, Gainesville, FL 32611
Shape-Based Inductive Bias for Glioma Grading from Tumor Contours
Jul 27, 2026Puneet Velidi, Michelle F. Miranda, Farouk Nathoo +2BraTS Geometric Representation Learning
Department of Mathematics and Statistics University of Victoria · Department of Computer Science University of Victoria · D´epartement de math´ematiques Universit´e du Qu´ebec `a Montr´eal
TVT-PAPD: Pathology-Aware Prototype Distillation for Self-Supervised Whole Slide Image Classification
Jul 11, 2026Ramesh Naidu Laveti, Jaya Sreevalsan-Nair, T K SrikanthPrototype Learning Histopathology Image Classification
E-Health Research Center, International Institute of Information Technology Bangalore, Karnataka 560100, India.
Generative Augmentation of Raman Spectra for Glioma Classification
Jul 11, 2026Andrei Iuşan, Iulian Vasile, Daria Voiculescu +4Synthetic Data Augmentation Raman Spectroscopy
National Institute of Research and Development for Biological Sciences, Romania · Department of Mathematics and Statistics, University of Turku, Finland · Research Institute for Artificial Intelligence “Mihai Dragănescu”, Romanian Academy +2
A Novel Machine Learning Approach for Central Nervous System Tumor Classification from DNA Methylation
Jul 1, 2026Paulo R. Ferreira, Lucas Coutinho Freitas, Laís dos Santos Gonçalves +4Multiclass Classification Oncology
Universidade Federal de Pelotas, Pelotas, Rio Grande do Sul, Brazil · Hospital de Clínicas de Porto Alegre, Porto Alegre, Rio Grande do Sul, Brazil
Automated brain tumor detection in MRI images using CNN and ResNet architectures
Jun 25, 2026Annapurna V K, Asha N, K Paramesha +2Convolutional Neural Networks Brain Tumor Classification
Department of Computer Science and Engineering, The National Institute of Engineering, Mysuru, Karnataka, India. · Department of MCA, The National Institute of Engineering, Mysuru, Karnataka, India. · Department of Computer Science and Engineering, Vidyavardhaka College of Engineering, Mysuru, Karnataka, India. +1
Multi-cancer detection using a computationally efficient CNN with transfer learning
Jun 21, 2026Vasileios E. Papageorgiou, Georgios Petmezas, Dimitrios-Panagiotis Papageorgiou +2Efficient Neural Network Inference Medical Image Classification
School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece · Department of Mathematics, National and Kapodistrian University of Athens, Athens, Greece · Department of Mathematics, Aristotle University of Thessaloniki, Thessaloniki, Greece +1
Multi-Class Brain Tumor Classification Using Advanced Deep Learning Models: A Comparative Study
Jun 17, 2026Asad Channa, Asghar Ali Chandio, Akhtar Hussain Jalbani +2Medical Image Classification Multiclass Classification
Department of Computer Science, Quaid-e-Awam University of Engineering, Sciences & Technology, Paksitan · Department of Artificial Intelligence, Quaid-e-Awam University of Engineering, Sciences & Technology, Pakistan · The Faculty of Artificial Intelligence and Cyber Security, Universiti Teknikal Malaysia Melaka +2
BrainFusionNet: a deep learning and XAI model to understand local, global, and sequential features of MRI images for improved brain tumour detection
Jun 17, 2026Md Taimur Ahad, Bo Song, Yan LiHybrid CNN-Transformer Architectures Explainable Medical Image Analysis
School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, QLD 4350, Australia · School of Engineering, University of Southern Queensland, Toowoomba, QLD 4350, Australia
Multimodal Brain Tumour Classification Using Feature Fusion
Jun 9, 2026Wajih ul Islam, Muhammad Yaqoob, Javed Ali Khan +1Multimodal Classification Medical Image Classification
School of Physics, Engineering and Computer Science, University of Hertfordshire, UK
Bridging Topology and Deep Representation Learning: A TDA-ViT Fusion Model for Four-Class Brain Tumor Classification
May 30, 2026Faisal AhmedVision Transformer Medical Image Classification
Department of Data Science and Mathematics, Embry-Riddle Aeronautical University, 3700 Willow Creek Rd, Prescott, 86301, Arizona, USA.
ConceptM $^3$ oE: Concept-Guided Multimodal Mixture of Experts for Interpretable Computational Pathology
May 23, 2026Xuan Wang, Zhongling Xu, Gopi Kannedhara +13Computational Pathology Concept Bottleneck Models
University of Texas at Austin, Austin, TX, USA · University of North Carolina at Chapel Hill, NC, USA · Dell Children’s Medical Center, Austin, TX, USA +2
Do Synthetic Brain MRIs Reliably Improve Tumour Classification? A StyleGAN2-ADA Class-Plane Augmentation Study on BRISC 2025
May 21, 2026José Rafael Noriega CedeñoSynthetic Data Augmentation Medical Image Classification
Faculty of Applied Sciences and Technology
Quantized Machine Learning Models for Medical Imaging in Low-Resource Healthcare Settings
May 19, 2026Sumanth Meenan Kanneti, Aryan ShahEfficient Neural Network Inference Medical Image Classification
Georgia State University · University of Southern California
Brain Tumor Classification in MRI Images: A Computationally Efficient Convolutional Neural Network
May 11, 2026Md Fahimul Kabir Chowdhury, Jannatul FerdousEfficient Neural Network Inference Medical Image Classification
Department of Computer Science and Engineering, University of North Texas, USA · Department of Electrical and Electronic Engineering, International Islamic University Chittagong, Bangladesh
Bridging visual saliency and large language models for explainable deep learning in medical imaging
May 7, 2026Paul Valery Nguezet, Elie Tagne Fute, Yusuf Brima +2Radiology Report Generation Medical Image Classification
Department of Mathematics and Computer science, University of Dschang, Dschang, 67, Cameroon. · Computer Vision, Institute of Cognitive Science, Osnabrück University, Osnabrueck, D-49090, Lower Saxony, Germany. · Department of Mathematics, Rhodes University, Grahamstown, Eastern Cape, 6140, Eastern Cape, South Africa. +1
Orientation-Aware Unsupervised Domain Adaptation for Brain Tumor Classification Across Multi-Modal MRI
May 5, 2026Sapna Sachan, Amulya Kumar Mahto, Prashant Wagambar PatilUnsupervised Domain Adaptation Medical Image Classification
Indian Institute of Technology, Guwahati
TumorXAI: Self-Supervised Deep Learning Framework for Explainable Brain MRI Tumor Classification
May 3, 2026Abrar Hossain Zahin, Amit Kumar Saha, Tanvir Mridha +5Self-Supervised Learning Medical Image Classification
Department of Computer Science and Engineering, East West University, Dhaka, 1212, Bangladesh.
A Data-Centric Framework for Intraoperative Fluorescence Lifetime Imaging for Glioma Surgical Guidance
Apr 28, 2026Silvia Noble Anbunesan, Mohamed Abul Hassan, Jinyi Qi +4Medical Image Analysis Medical Image Classification
University of California, Davis · Department of Biomedical Engineering · Department of Pathology and Laboratory Medicine +1
CNN-ViT Fusion with Adaptive Attention Gate for Brain Tumor MRI Classification: A Hybrid Deep Learning Model
Apr 25, 2026Syed Ibad Hasnain, Muhammad Faris, Hafiza Syeda Yusra Tirmizi +2Hybrid CNN-Transformer Architectures Medical Image Classification
Department of Biomedical Engineering Hamdard University Karachi, Pakistan. · Department of Biomedical Engineering Sir Syed University Karachi, Pakistan.
an interpretable vision transformer framework for automated brain tumor classification
Apr 23, 2026Chinedu Emmanuel Mbonu, Tochukwu Sunday Belonwu, Okwuchukwu Ejike Chukwuogo +1Vision Transformer Medical Image Classification
Department of Computer Science, Nazarbayev University, Astana · Department of Computer Science, Nnamdi Azikiwe University, Nigeria
Clinically-Informed Modeling for Pediatric Brain Tumor Classification from Whole-Slide Histopathology Images
Apr 22, 2026Joakim Nguyen, Jian Yu, Jinrui Fang +7Pathology Foundation Models Medical Image Classification
Dept. of Computer Science University of Texas at Austin Austin, TX, USA · School of Information University of Texas at Austin Austin, TX, USA · Dept. of Computer Science UNC at Chapel Hill Chapel Hill, NC, USA +3
Chaos-Enhanced Prototypical Networks for Few-Shot Medical Image Classification
Apr 19, 2026Chinthakuntla Meghan Sai, Murarisetty V Sai Kartheek, Sita Devi Bharatula +1Prototype-Based Classification Brain Tumor Classification
Department of ECE, Amrita Vishwa Vidyapeetham, Vengal, Chennai, 601103, Tamil Nadu, India. · Hydronium Energies Limited, Bristol, London, England.

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Scores That Hold, Benchmarks That Leak: Measuring Dataset Contamination in Public Brain-Tumor MRI Classification

Bhanu Prakash Vangala, Sowmya Guda, Latha Peddi, Navya Vangala

Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211 USA · Government Degree College (Autonomous), Siddipet, Telangana 502103, India · Amar Biotech Private Limited, Kondapur, Hyderabad, Telangana 500084, India

Data Leakage Benchmark Contamination Brain Tumor Classification Medical Image Benchmarks Brain MRI

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Automated classification of brain tumors from MRI is a heavily published application of deep learning in medical imaging, with reported accuracies on public benchmarks routinely exceeding 98%. However, accuracy does not capture a critical dimension of benchmark quality: dataset integrity, defined as the independence of test from training data at the image, patient, and acquisition-source levels. We introduce a three-layer contamination framework comprising duplicate, patient, and source-label leakage to assess the public corpora on which this literature rests. We audit the three most widely used corpora against a chest-radiograph negative control and quantify each layer's effect on measured performance across nine architectures and three evaluation conditions. Contamination is severe at every layer: 28.8% of the dominant corpus's official test split has a near-twin in its own training split, a second corpus leaks 22.3% of its test images byte-identically, 95.5% of traceable test images share a patient with training, and file-header features containing no anatomy separate tumor from no-tumor at 0.959 balanced accuracy, at parity with fine-tuned ResNet backbones. The unexpected result is that removing every identified leaked test image leaves balanced accuracy essentially unchanged: stable performance after deduplication does not establish benchmark integrity. Our findings establish dataset integrity as a distinct, measurable axis of benchmark quality that a stable leaderboard cannot certify. For biomedical research, reported accuracy on these corpora alone does not establish that a model has learned to recognize tumors rather than exploit dataset-specific cues. We release the contaminated-file lists, recovered patient identifiers, and deduplicated splits.

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