ArticleJournal of medical systems2019
Brain Tumor Segmentation Based on Improved Convolutional Neural Network in Combination with Non-quantifiable Local Texture Feature.
Article in Journal of medical systems, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Application and Development of Deep Learning in Radiotherapy: A Systematic Review.Technology in cancer research & treatmentPooled it
- Advances in Artificial Intelligence for Glioblastoma Radiotherapy Planning and Treatment.Cancers · 2025Review
- Deep learning driven interpretable and informed decision making model for brain tumour prediction using explainable AI.Scientific reports · 2025Article
- Context aware machine learning techniques for brain tumor classification and detection - A review.Heliyon · 2025Review
- Deep learning for automated segmentation in radiotherapy: a narrative review.The British journal of radiology · 2024Review
- Enhancing brain tumor diagnosis: an optimized CNN hyperparameter model for improved accuracy and reliability.PeerJ. Computer science · 2024Article
- An early detection and segmentation of Brain Tumor using Deep Neural Network.BMC medical informatics and decision making · 2023Article
- Human identification performed with skull's sphenoid sinus based on deep learning.International journal of legal medicine · 2022Article
- A Comprehensive Analysis of Recent Deep and Federated-Learning-Based Methodologies for Brain Tumor Diagnosis.Journal of personalized medicine · 2022Review
- Review
- Risk Factors of Restroke in Patients with Lacunar Cerebral Infarction Using Magnetic Resonance Imaging Image Features under Deep Learning Algorithm.Contrast media & molecular imaging · 2021Article
- The Application of Deep Convolutional Neural Networks to Brain Cancer Images: A Survey.Journal of personalized medicine · 2020Review
- Radiomics in radiation oncology-basics, methods, and limitations.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2020Review
- A drug identification model developed using deep learning technologies: experience of a medical center in Taiwan.BMC health services research · 2020Article
- Brain Tumor Analysis Empowered with Deep Learning: A Review, Taxonomy, and Future Challenges.Brain sciences · 2020Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate and reliable brain tumor segmentation is a critical component in cancer diagnosis. According to deep learning model, a novel brain tumor segmentation method is developed by integrating fully convolutional neural networks (FCNN) and dense micro-block difference feature (DMDF) into a unified framework so as to obtain segmentation results with appearance and spatial consistency. Firstly, we propose a local feature to describe the rotation invariant property of the texture. In order to deal with the change of rotation and scale in texture image, Fisher vector encoding method is used to analyze the texture feature, which can combine with the scale information without increasing the dimension of the local feature. The obtained local features have strong robustness to rotation and gray intensity variation. Then, the non-quantifiable local feature is fused to the FCNN to perform fine boundary segmentation. Since brain tumors occupy a small portion of the image, deconvolutional layers are designed with skip connections to obtain a high quality feature map. Compared with the traditional MRI brain tumor segmentation methods, the experimental results show that the segmentation accuracy and stability has been greatly improved. Average Dice index can be up to 90.98%. And the proposed method has very high real-time performance, where brain tumor image can segment within 1 s.
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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.