mlsanityData to 7 Oct 2026

Brain Tumor MRI Classification using a Novel Deep Residual and Regional CNN

arXiv 2211.16571 ↗First posted 29 Nov 2022eess.IV cs.CV cs.LGcited by 7

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Abstract (arXiv)

Brain tumor classification is crucial for clinical analysis and an effective treatment plan to cure patients. Deep learning models help radiologists to accurately and efficiently analyze tumors without manual intervention. However, brain tumor analysis is challenging because of its complex structure, texture, size, location, and appearance. Therefore, a novel deep residual and regional-based Res-BRNet Convolutional Neural Network (CNN) is developed for effective brain tumor (Magnetic Resonance Imaging) MRI classification. The developed Res-BRNet employed Regional and boundary-based operations in a systematic order within the modified spatial and residual blocks. Moreover, the spatial block extract homogeneity and boundary-defined features at the abstract level. Furthermore, the residual blocks employed at the target level significantly learn local and global texture variations of different classes of brain tumors. The efficiency of the developed Res-BRNet is evaluated on a standard dataset; collected from Kaggle and Figshare containing various tumor categories, including meningioma, glioma, pituitary, and healthy images. Experiments prove that the developed Res-BRNet outperforms the standard CNN models and attained excellent performances (accuracy: 98.22%, sensitivity: 0.9811, F-score: 0.9841, and precision: 0.9822) on challenging datasets. Additionally, the performance of the proposed Res-BRNet indicates a strong potential for medical image-based disease analyses.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 17 of its 65 references that are in the map. First posted on arXiv 29 Nov 2022.

Builds on

17 of 65 references.

References · 17

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5 more
  1. 2022A Survey of Deep Learning Techniques for the Analysis of COVID-19 and their usability for Detecting Omicroncited by 13
  2. 2022COVID-19 Detection and Analysis From Lung CT Images using Novel Channel Boosted CNNscited by 12
  3. 2020Classification and Region Analysis of COVID-19 Infection using Lung CT Images and Deep Convolutional Neural Networkscited by 11
  4. 2022A New Deep Hybrid Boosted and Ensemble Learning-based Brain Tumor Analysis using MRIcited by 10
  5. 2022IoT Malware Detection Architecture using a Novel Channel Boosted and Squeezed CNNcited by 10

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 3; 2025: 2; 2026: 1.

7 papers.

  1. 2023COVID-19 Infection Analysis Framework using Novel Boosted CNNs and Radiological Imagescited by 4
  2. 2024RS-FME-SwinT: A Novel Feature Map Enhancement Framework Integrating Customized SwinT with Residual and Spatial CNN for Monkeypox Diagnosiscited by 4
  3. 2025RS-CA-HSICT: A Residual and Spatial Channel Augmented CNN Transformer Framework for Monkeypox Detectioncited by 3
  4. 2024A Novel Feature Map Enhancement Technique Integrating Residual CNN and Transformer for Alzheimer Diseases Diagnosiscited by 2
  5. 2026A Tumor Aware DenseNet Swin Hybrid Learning with Boosted and Hierarchical Feature Spaces for Large-Scale Brain MRI Classificationcited by 1
  6. 2024A Novel Decision Ensemble Framework: Customized Attention-BiLSTM and XGBoost for Speculative Stock Price Forecastingcited by 0
  7. 2025Intelligent Systems in Neuroimaging: Pioneering AI Techniques for Brain Tumor Detectioncited by 0