ArticleComputational and mathematical methods in medicine2022
Early Diagnosis of Brain Tumour MRI Images Using Hybrid Techniques between Deep and Machine Learning.
Article in Computational and mathematical methods in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 2 of them syntheses that pooled it.
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Who cites it
46 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Advanced applications in chronic disease monitoring using IoT mobile sensing device data, machine learning algorithms and frame theory: a systematic review.Frontiers in public health · 2025Pooled it
- A systematic review of the hybrid machine learning models for brain tumour segmentation and detection in medical images.Frontiers in artificial intelligence · 2025Pooled it
- Hybrid deep neural network with PCA based features optimization for enhancing brain tumor classification.Scientific reports · 2026Article
- Brain Tumour Classification Model Based on Spatial Block-Residual Block Collaborative Architecture with Strip Pooling Feature Fusion.Journal of imaging · 2025Article
- A Hybrid Model of Feature Extraction and Dimensionality Reduction Using ViT, PCA, and Random Forest for Multi-Classification of Brain Cancer.Diagnostics (Basel, Switzerland) · 2025Article
- Automatic smart brain tumor classification and prediction system using deep learning.Scientific reports · 2025Article
- Brain tumor segmentation and detection in MRI using convolutional neural networks and VGG16.Cancer biomarkers : section A of Disease markers · 2025Article
- Emerging Wearable Acoustic Sensing Technologies.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- MWG-UNet++: Hybrid Transformer U-Net Model for Brain Tumor Segmentation in MRI Scans.Bioengineering (Basel, Switzerland) · 2025Article
- Deep learning-based classification of multiple fundus diseases using ultra-widefield images.Frontiers in cell and developmental biology · 2025Article
- EnSLDe: an enhanced short-range and long-range dependent system for brain tumor classification.Frontiers in oncology · 2025Article
- Challenges and Advances in Classifying Brain Tumors: An Overview of Machine, Deep Learning, and Hybrid Approaches with Future Perspectives in Medical Imaging.Current medical imaging · 2025Review
- Enhancing privacy-preserving brain tumor classification with adaptive reputation-aware federated learning and homomorphic encryption.PeerJ. Computer science · 2025Article
- Federated Learning with Privacy Preserving for Multi- Institutional Three-Dimensional Brain Tumor Segmentation.Diagnostics (Basel, Switzerland) · 2024Article
- Efficient and Accurate Brain Tumor Classification Using Hybrid MobileNetV2-Support Vector Machine for Magnetic Resonance Imaging Diagnostics in Neoplasms.Brain sciences · 2024Article
- Brain Tumor MRI Classification Using a Novel Deep Residual and Regional CNN.Biomedicines · 2024Article
- ASD-SWNet: a novel shared-weight feature extraction and classification network for autism spectrum disorder diagnosis.Scientific reports · 2024Article
- The Role of ArtificiaI Intelligence in Brain Tumor Diagnosis: An Evaluation of a Machine Learning Model.Cureus · 2024Article
- Advances in the Use of Deep Learning for the Analysis of Magnetic Resonance Image in Neuro-Oncology.Cancers · 2024Review
- A Deep Learning Based Intelligent Decision Support System for Automatic Detection of Brain Tumor.Biomedical engineering and computational biology · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer is considered one of the most aggressive and destructive diseases that shortens the average lives of patients. Misdiagnosed brain tumours lead to false medical intervention, which reduces patients' chance of survival. Accurate early medical diagnoses of brain tumour are an essential point for starting treatment plans that improve the survival of patients with brain tumours. Computer-aided diagnostic systems have provided consecutive successes for helping medical doctors make accurate diagnoses and have conducted positive strides in the field of deep and machine learning. Deep convolutional layers extract strong distinguishing features from the regions of interest compared with those extracted using traditional methods. In this study, different experiments are performed for brain tumour diagnosis by combining deep learning and traditional machine learning techniques. AlexNet and ResNet-18 are used with the support vector machine (SVM) algorithm for brain tumour classification and diagnosis. Brain tumour magnetic resonance imaging (MRI) images are enhanced using the average filter technique. Then, deep learning techniques are applied to extract robust and important deep features via deep convolutional layers. The process of combining deep and machine learning techniques starts, where features are extracted using deep learning techniques, namely, AlexNet and ResNet-18. These features are then classified using SoftMax and SVM. The MRI dataset contains 3,060 images divided into four classes, which are three tumours and one normal. All systems have achieved superior results. Specifically, the AlexNet+SVM hybrid technique exhibits the best performance, with 95.10% accuracy, 95.25% sensitivity, and 98.50% specificity.
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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.