ArticleComputational intelligence and neuroscience2022
BrainNet: Optimal Deep Learning Feature Fusion for Brain Tumor Classification.
Article in Computational intelligence and neuroscience, 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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17 citing papers in PubMed.
- Comparative Analysis of Transfer Learning Based Residual Networks for the Brain Tumours Detection.Annals of neurosciences · 2026Article
- Predicting preoperative lymph node metastasis of hilar cholangiocarcinoma based on deep learning radiomics.The ultrasound journal · 2026Article
- A Hybrid Lung and Colon Histopathological Image Classification Framework Using MobileNetV3-Small Deep Features and Differential Evolution Optimization.Diagnostics (Basel, Switzerland) · 2026Article
- Deep transfer learning based feature fusion model with Bonobo optimization algorithm for enhanced brain tumor segmentation and classification through biomedical imaging.Scientific reports · 2025Article
- Improved brain tumor classification through DenseNet121 based transfer learning.Discover oncology · 2025Article
- AG-MSTLN-EL: A Multi-source Transfer Learning Approach to Brain Tumor Detection.Journal of imaging informatics in medicine · 2025Article
- BrainNet: a fusion assisted novel optimal framework of residual blocks and stacked autoencoders for multimodal brain tumor classification.Scientific reports · 2024Article
- Article
- Enhanced brain tumor diagnosis using combined deep learning models and weight selection technique.Frontiers in neuroinformatics · 2024Article
- Efficient Skip Connections-Based Residual Network (ESRNet) for Brain Tumor Classification.Diagnostics (Basel, Switzerland) · 2023Article
- BU-DLNet: Breast Ultrasonography-Based Cancer Detection Using Deep-Learning Network Selection and Feature Optimization.Bioengineering (Basel, Switzerland) · 2023Article
- A Lightweight Deep Learning Based Microwave Brain Image Network Model for Brain Tumor Classification Using Reconstructed Microwave Brain (RMB) Images.Biosensors · 2023Article
- Automatic Intelligent System Using Medical of Things for Multiple Sclerosis Detection.Computational intelligence and neuroscience · 2023Article
- Predicting Breast Cancer Leveraging Supervised Machine Learning Techniques.Computational and mathematical methods in medicine · 2022Article
- A Novel CNN pooling layer for breast cancer segmentation and classification from thermograms.PloS one · 2022Article
- Article
- Analysis of Hybrid Feature Optimization Techniques Based on the Classification Accuracy of Brain Tumor Regions Using Machine Learning and Further Evaluation Based on the Institute Test Data.Journal of medical physicsArticle
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7 authors.
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Abstract
Early detection of brain tumors can save precious human life. This work presents a fully automated design to classify brain tumors. The proposed scheme employs optimal deep learning features for the classification of FLAIR, T1, T2, and T1CE tumors. Initially, we normalized the dataset to pass them to the ResNet101 pretrained model to perform transfer learning for our dataset. This approach results in fine-tuning the ResNet101 model for brain tumor classification. The problem with this approach is the generation of redundant features. These redundant features degrade accuracy and cause computational overhead. To tackle this problem, we find optimal features by utilizing differential evaluation and particle swarm optimization algorithms. The obtained optimal feature vectors are then serially fused to get a single-fused feature vector. PCA is applied to this fused vector to get the final optimized feature vector. This optimized feature vector is fed as input to various classifiers to classify tumors. Performance is analyzed at various stages. Performance results show that the proposed technique achieved a speedup of 25.5x in prediction time on the medium neural network with an accuracy of 94.4%. These results show significant improvement over the state-of-the-art techniques in terms of computational overhead by maintaining approximately the same accuracy.
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