ORB-SVM : An Innovative Hybrid Framework for Efficient Brain Tumor Detection from MRI Scans

• URL: http://arxiv.org/abs/2609.02333v1
• Date: Wed, 02 Sep 2026 09:09:14 GMT
• Title: ORB-SVM : An Innovative Hybrid Framework for Efficient Brain Tumor Detection from MRI Scans
• Abstract summary: This study introduces a hybrid framework that utilizes the Oriented FAST and Rotated BRIEF (ORB) algorithm for precise feature extraction and a Support Vector Machine (SVM) for classification.<n>The proposed approach achieves a sub- stantial data reduction of approximately 99.5%, which effectively minimizes the influence of non informative background data.<n> Experimental evaluations conducted on the Br35H dataset demonstrate that the framework attains a classification accuracy of 97.5%.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Brain cancer remains one of the most significant challenges in modern medicine, where the accuracy of early stage diagnosis is a decisive factor in patient survival and treatment efficacy. Although Magnetic Resonance Imaging (MRI) is the established gold standard for visualizing neurological structures, the interpretation of these high dimensional scans is often complicated by subjective variability among practitioners and the inherent noise present in complex medical images. While contemporary approaches frequently rely on high parameter deep learning architectures, such models often involve significant computational costs and require extensive data for effective training. This study introduces a hybrid framework that utilizes the Oriented FAST and Rotated BRIEF (ORB) algorithm for precise feature extraction and a Support Vector Machine (SVM) for classification [1], [2]. The proposed approach achieves a sub- stantial data reduction of approximately 99.5%, which effectively minimizes the influence of non informative background data while preserving critical diagnostic patterns essential for tumor identification. By balancing feature sparsity with a robust kernel based classifier, this methodology addresses the limitations of over parameterized systems while maintaining high diagnostic integrity. Experimental evaluations conducted on the Br35H dataset demonstrate that the framework attains a classification accuracy of 97.5%. The findings suggest that the integration of localized feature representation and optimized classification provides a reliable and resource efficient alternative for medical image analysis, offering a structured solution that maintains per- formance without the need for extensive computational overhead.

Related papers

• Deep Learning-Based Pneumonia Detection from Chest X-ray Images: A CNN Approach with Performance Analysis and Clinical Implications [0.0]
  The study introduces an intricate deep learning system using Conal Neural Networks for automated pneumonia detection from chest Xray images.<n>The proposed CNN architecture integrates sophisticated methods including separable convolutions along with batch normalization and dropout regularization.<n>A convoluted array of evaluation metrics such as accuracy, precision, recall, and F1 score collectively verify the model exceptional performance by recording an accuracy rate of 91.
  arXiv  Detail & Related papers  (2025-09-26T11:58:50Z)
• From Promise to Practical Reality: Transforming Diffusion MRI Analysis with Fast Deep Learning Enhancement [35.368152968098194]
  FastFOD-Net is an end-to-end deep learning framework enhancing FODs with superior performance and delivering training/inference efficiency for clinical use.<n>This work will facilitate the more widespread adoption of, and build clinical trust in, deep learning based methods for diffusion MRI enhancement.
  arXiv  Detail & Related papers  (2025-08-13T17:56:29Z)
• Attend-and-Refine: Interactive keypoint estimation and quantitative cervical vertebrae analysis for bone age assessment [32.52024944963992]
  In pediatric orthodontics, accurate estimation of growth potential is essential for developing effective treatment strategies.<n>This research aims to predict this potential by identifying the growth peak and analyzing cervical vertebra morphology solely through lateral cephalometric radiographs.<n>We introduce ARNet, a user-interactive, deep learning-based model designed to streamline the annotation process.
  arXiv  Detail & Related papers  (2025-07-10T11:52:20Z)
• Hierarchical Deep Feature Fusion and Ensemble Learning for Enhanced Brain Tumor MRI Classification [3.776159955137874]
  The framework incorporates comprehensive preprocessing and data augmentation of brain magnetic resonance images (MRI)<n>The novelty lies in the dual-level ensembling strategy: feature-level ensembling, and classifier-level ensembling.<n> Experiments on two public Kaggle MRI brain tumor datasets demonstrate that this approach significantly surpasses state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-06-14T05:53:54Z)
• Enhancing Orthopox Image Classification Using Hybrid Machine Learning and Deep Learning Models [40.325359811289445]
  This paper uses Machine Learning models combined with pretrained Deep Learning models to extract deep feature representations without the need for augmented data.<n>The findings show that this feature extraction method, when paired with other methods in the state-of-the-art, produces excellent classification outcomes.
  arXiv  Detail & Related papers  (2025-06-06T11:52:07Z)
• Low-Resolution Chest X-ray Classification via Knowledge Distillation and Multi-task Learning [46.75992018094998]
  This research addresses the challenges of diagnosing chest X-rays (CXRs) at low resolutions. High-resolution CXR imaging is crucial for identifying small but critical anomalies, such as nodules or opacities. This paper presents the Multilevel Collaborative Attention Knowledge (MLCAK) method.
  arXiv  Detail & Related papers  (2024-05-22T06:10:54Z)
• Empowering Healthcare through Privacy-Preserving MRI Analysis [3.6394715554048234]
  We introduce the Ensemble-Based Federated Learning (EBFL) Framework. EBFL framework deviates from the conventional approach by emphasizing model features over sharing sensitive patient data. We have achieved remarkable precision in the classification of brain tumors, including glioma, meningioma, pituitary, and non-tumor instances.
  arXiv  Detail & Related papers  (2024-03-14T19:51:18Z)
• UniBrain: Universal Brain MRI Diagnosis with Hierarchical Knowledge-enhanced Pre-training [66.16134293168535]
  We propose a hierarchical knowledge-enhanced pre-training framework for the universal brain MRI diagnosis, termed as UniBrain. Specifically, UniBrain leverages a large-scale dataset of 24,770 imaging-report pairs from routine diagnostics.
  arXiv  Detail & Related papers  (2023-09-13T09:22:49Z)
• An Optimized Ensemble Deep Learning Model For Brain Tumor Classification [3.072340427031969]
  Inaccurate identification of brain tumors can significantly diminish life expectancy.<n>This study introduces an innovative optimization-based deep ensemble approach employing transfer learning (TL) to efficiently classify brain tumors.<n>Our approach achieves notable accuracy scores, with Xception, ResNet50V2, ResNet152V2, InceptionResNetV2, GAWO, and GSWO attaining 99.42%, 98.37%, 98.22%, 98.26%, 99.71%, and 99.76% accuracy, respectively.
  arXiv  Detail & Related papers  (2023-05-22T09:08:59Z)
• Brain Imaging-to-Graph Generation using Adversarial Hierarchical Diffusion Models for MCI Causality Analysis [44.45598796591008]
  Brain imaging-to-graph generation (BIGG) framework is proposed to map functional magnetic resonance imaging (fMRI) into effective connectivity for mild cognitive impairment analysis. The hierarchical transformers in the generator are designed to estimate the noise at multiple scales. Evaluations of the ADNI dataset demonstrate the feasibility and efficacy of the proposed model.
  arXiv  Detail & Related papers  (2023-05-18T06:54:56Z)
• Diagnose Like a Radiologist: Hybrid Neuro-Probabilistic Reasoning for Attribute-Based Medical Image Diagnosis [42.624671531003166]
  We introduce a hybrid neuro-probabilistic reasoning algorithm for verifiable attribute-based medical image diagnosis. We have successfully applied our hybrid reasoning algorithm to two challenging medical image diagnosis tasks.
  arXiv  Detail & Related papers  (2022-08-19T12:06:46Z)
• Computational Intelligence Approach to Improve the Classification Accuracy of Brain Neoplasm in MRI Data [8.980876474818153]
  This report presents two improvements for brain neoplasm detection in MRI data. An advanced preprocessing technique is proposed to improve the area of interest in MRI data. A hybrid technique using CNN for feature extraction followed by Support Vector Machine (SVM) for classification is also proposed.
  arXiv  Detail & Related papers  (2021-01-24T06:45:26Z)
• Brain Tumor Anomaly Detection via Latent Regularized Adversarial Network [34.81845999071626]
  We propose an innovative brain tumor abnormality detection algorithm. The semi-supervised anomaly detection model is proposed in which only healthy (normal) brain images are trained.
  arXiv  Detail & Related papers  (2020-07-09T12:12:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.