ArticleLife (Basel, Switzerland)2023
Brain Tumor Detection and Classification Using Fine-Tuned CNN with ResNet50 and U-Net Model: A Study on TCGA-LGG and TCIA Dataset for MRI Applications.
Article in Life (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- A Class-Wise Deep Ensemble Framework Using ResNet101 and DenseNet201 for Brain Tumor Classification.Journal of imaging informatics in medicine · 2026Article
- A multi-level attention CNN-transformer based framework for the detection of brain tumor using regional dual-score explainability.Scientific reports · 2026Article
- A framework for a national cancer imaging repository in Nigeria.Scientific reports · 2026Article
- An Efficient and Cohesive System for Enhanced Accuracy in Malignant Brain Tumor Diagnosis.Current medical imaging · 2026Article
- Article
- XcepFusion for brain tumor detection using a hybrid transfer learning framework with layer pruning and freezing.Scientific reports · 2025Article
- A novel sub-differentiable hausdorff loss combined with BCE for MRI brain tumor segmentation using UNet variants.Scientific reports · 2025Article
- Enhancing brain tumor segmentation using attention based convolutional UNet on MRI images.Scientific reports · 2025Article
- Explainable Deep Ensemble Meta-Learning Framework for Brain Tumor Classification Using MRI Images.Cancers · 2025Article
- Hybrid Attention-Enhanced Xception and Dynamic Chaotic Whale Optimization for Brain Tumor Diagnosis.Bioengineering (Basel, Switzerland) · 2025Article
- A novel hybrid vision UNet architecture for brain tumor segmentation and classification.Scientific reports · 2025Article
- Machine learning fusion for glioma tumor detection.Scientific reports · 2025Article
- Dual-Stream Contrastive Latent Learning Generative Adversarial Network for Brain Image Synthesis and Tumor Classification.Journal of imaging · 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
- A multi-modal prompt-tuning method of ultrasound diagnosis for thyroid nodule.Frontiers in medicine · 2025Article
- A Unified Pipeline for Simultaneous Brain Tumor Classification and Segmentation Using Fine-Tuned CNN and Residual UNet Architecture.Life (Basel, Switzerland) · 2024Article
- Review
- Enhancing brain tumor diagnosis: an optimized CNN hyperparameter model for improved accuracy and reliability.PeerJ. Computer science · 2024Article
- Advancing Brain Tumor Classification through Fine-Tuned Vision Transformers: A Comparative Study of Pre-Trained Models.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
11 authors.
Funding
Abstract
Nowadays, brain tumors have become a leading cause of mortality worldwide. The brain cells in the tumor grow abnormally and badly affect the surrounding brain cells. These cells could be either cancerous or non-cancerous types, and their symptoms can vary depending on their location, size, and type. Due to its complex and varying structure, detecting and classifying the brain tumor accurately at the initial stages to avoid maximum death loss is challenging. This research proposes an improved fine-tuned model based on CNN with ResNet50 and U-Net to solve this problem. This model works on the publicly available dataset known as TCGA-LGG and TCIA. The dataset consists of 120 patients. The proposed CNN and fine-tuned ResNet50 model are used to detect and classify the tumor or no-tumor images. Furthermore, the U-Net model is integrated for the segmentation of the tumor regions correctly. The model performance evaluation metrics are accuracy, intersection over union, dice similarity coefficient, and similarity index. The results from fine-tuned ResNet50 model are IoU: 0.91, DSC: 0.95, SI: 0.95. In contrast, U-Net with ResNet50 outperforms all other models and correctly classified and segmented the tumor region.
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