ArticleDiagnostics (Basel, Switzerland)2022
Isolated Convolutional-Neural-Network-Based Deep-Feature Extraction for Brain Tumor Classification Using Shallow Classifier.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Machine Learning in MRI Brain Imaging: A Review of Methods, Challenges, and Future Directions.Diagnostics (Basel, Switzerland) · 2025Review
- Learning Architecture for Brain Tumor Classification Based on Deep Convolutional Neural Network: Classic and ResNet50.Diagnostics (Basel, Switzerland) · 2025Article
- EnSLDe: an enhanced short-range and long-range dependent system for brain tumor classification.Frontiers in oncology · 2025Article
- MRI-Driven Alzheimer's Disease Diagnosis Using Deep Network Fusion and Optimal Selection of Feature.Bioengineering (Basel, Switzerland) · 2024Article
- Advances in the Use of Deep Learning for the Analysis of Magnetic Resonance Image in Neuro-Oncology.Cancers · 2024Review
- TTDCapsNet: Tri Texton-Dense Capsule Network for complex and medical image recognition.PloS one · 2024Article
- ContourTL-Net: Contour-Based Transfer Learning Algorithm for Early-Stage Brain Tumor Detection.International journal of biomedical imaging · 2024Article
- Enhancing Alzheimer's disease diagnosis and staging: a multistage CNN framework using MRI.Frontiers in psychiatry · 2024Article
- Article
- BU-DLNet: Breast Ultrasonography-Based Cancer Detection Using Deep-Learning Network Selection and Feature Optimization.Bioengineering (Basel, Switzerland) · 2023Article
- WBM-DLNets: Wrapper-Based Metaheuristic Deep Learning Networks Feature Optimization for Enhancing Brain Tumor Detection.Bioengineering (Basel, Switzerland) · 2023Article
- Blockchain-Based Deep CNN for Brain Tumor Prediction Using MRI Scans.Diagnostics (Basel, Switzerland) · 2023Article
- An Ensemble Model for the Diagnosis of Brain Tumors through MRIs.Diagnostics (Basel, Switzerland) · 2023Article
- Role of Ensemble Deep Learning for Brain Tumor Classification in Multiple Magnetic Resonance Imaging Sequence Data.Diagnostics (Basel, Switzerland) · 2023Article
- Dendrimer: An update on recent developments and future opportunities for the brain tumors diagnosis and treatment.Frontiers in pharmacology · 2023Review
- Multi-Stage Temporal Convolutional Network with Moment Loss and Positional Encoding for Surgical Phase Recognition.Diagnostics (Basel, Switzerland) · 2022Article
- A Robust Computer-Aided Automated Brain Tumor Diagnosis Approach Using PSO-ReliefF Optimized Gaussian and Non-Linear Feature Space.Life (Basel, Switzerland) · 2022Article
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Authors and funding
11 authors.
Funding
Abstract
In today's world, a brain tumor is one of the most serious diseases. If it is detected at an advanced stage, it might lead to a very limited survival rate. Therefore, brain tumor classification is crucial for appropriate therapeutic planning to improve patient life quality. This research investigates a deep-feature-trained brain tumor detection and differentiation model using classical/linear machine learning classifiers (MLCs). In this study, transfer learning is used to obtain deep brain magnetic resonance imaging (MRI) scan features from a constructed convolutional neural network (CNN). First, multiple layers (19, 22, and 25) of isolated CNNs are constructed and trained to evaluate the performance. The developed CNN models are then utilized for training the multiple MLCs by extracting deep features via transfer learning. The available brain MRI datasets are employed to validate the proposed approach. The deep features of pre-trained models are also extracted to evaluate and compare their performance with the proposed approach. The proposed CNN deep-feature-trained support vector machine model yielded higher accuracy than other commonly used pre-trained deep-feature MLC training models. The presented approach detects and distinguishes brain tumors with 98% accuracy. It also has a good classification rate (97.2%) for an unknown dataset not used to train the model. Following extensive testing and analysis, the suggested technique might be helpful in assisting doctors in diagnosing brain tumors.
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Registered trials
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.