ArticleComputational and mathematical methods in medicine2020
An Intelligent Diagnosis Method of Brain MRI Tumor Segmentation Using Deep Convolutional Neural Network and SVM Algorithm.
Article in Computational and mathematical methods in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed.
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- A hybrid framework for colorectal cancer detection and U-Net segmentation using polynetDWTCADx.Scientific reports · 2025Article
- Enhanced Brain Tumor Segmentation Using CBAM-Integrated Deep Learning and Area Quantification.International journal of biomedical imaging · 2025Article
- Enhanced WGAN Model for Diagnosing Laryngeal Carcinoma.Cancers · 2024Article
- Segmentation method of magnetic resonance imaging brain tumor images based on improved UNet network.Translational cancer research · 2024Article
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- A New Medical Analytical Framework for Automated Detection of MRI Brain Tumor Using Evolutionary Quantum Inspired Level Set Technique.Bioengineering (Basel, Switzerland) · 2023Article
- RETRACTED ARTICLE: An automatic and intelligent brain tumor detection using Lee sigma filtered histogram segmentation model.Soft computing · 2023Article
- Evaluation of computed tomography images under deep learning in the diagnosis of severe pulmonary infection.Frontiers in computational neuroscience · 2023Article
- Comparison of Fine-Tuned Deep Convolutional Neural Networks for the Automated Classification of Lung Cancer Cytology Images with Integration of Additional Classifiers.Asian Pacific journal of cancer prevention : APJCP · 2022Article
- Brain tumor segmentation in MRI images using nonparametric localization and enhancement methods with U-net.International journal of computer assisted radiology and surgery · 2022Article
- Self-assessment and deep learning-based coronavirus detection and medical diagnosis systems for healthcare.Multimedia systems · 2022Article
- Early Diagnosis of Brain Tumour MRI Images Using Hybrid Techniques between Deep and Machine Learning.Computational and mathematical methods in medicine · 2022Article
- ColoRectalCADx: Expeditious Recognition of Colorectal Cancer with Integrated Convolutional Neural Networks and Visual Explanations Using Mixed Dataset Evidence.Computational and mathematical methods in medicine · 2022Article
- Dexterous Identification of Carcinoma through ColoRectalCADx with Dichotomous Fusion CNN and UNet Semantic Segmentation.Computational intelligence and neuroscience · 2022Article
- An efficient brain tumor image classifier by combining multi-pathway cascaded deep neural network and handcrafted features in MR images.Medical & biological engineering & computing · 2021Article
- 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
- Self-Learning Network-based segmentation for real-time brain M.R. images through HARIS.PeerJ. Computer science · 2021Article
- Magnetic resonance image-based brain tumour segmentation methods: A systematic review.Digital healthReview
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Authors and funding
8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Among the currently proposed brain segmentation methods, brain tumor segmentation methods based on traditional image processing and machine learning are not ideal enough. Therefore, deep learning-based brain segmentation methods are widely used. In the brain tumor segmentation method based on deep learning, the convolutional network model has a good brain segmentation effect. The deep convolutional network model has the problems of a large number of parameters and large loss of information in the encoding and decoding process. This paper proposes a deep convolutional neural network fusion support vector machine algorithm (DCNN-F-SVM). The proposed brain tumor segmentation model is mainly divided into three stages. In the first stage, a deep convolutional neural network is trained to learn the mapping from image space to tumor marker space. In the second stage, the predicted labels obtained from the deep convolutional neural network training are input into the integrated support vector machine classifier together with the test images. In the third stage, a deep convolutional neural network and an integrated support vector machine are connected in series to train a deep classifier. Run each model on the BraTS dataset and the self-made dataset to segment brain tumors. The segmentation results show that the performance of the proposed model is significantly better than the deep convolutional neural network and the integrated SVM classifier.
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