[go: up one dir, main page]

Shiny, 2024 - Google Patents

Brain tumor segmentation and classification using optimized U-Net

Shiny, 2024

Document ID
620544681897632786
Author
Shiny K
Publication year
Publication venue
The Imaging Science Journal

External Links

Snippet

This paper presents an optimization-driven classifier for classifying the brain tumour considering MRI. Here, the pre-operative and post-operative MRI is subjected to pre- processing, which is performed using filtering and Region of Interest (RoI) extraction …
Continue reading at www.tandfonline.com (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/34—Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
    • G06F19/345—Medical expert systems, neural networks or other automated diagnosis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/32—Medical data management, e.g. systems or protocols for archival or communication of medical images, computerised patient records or computerised general medical references
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46—Extraction of features or characteristics of the image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/02—Knowledge representation
    • G06N5/022—Knowledge engineering, knowledge acquisition

Similar Documents

Publication Publication Date Title
Mahmood et al. Breast lesions classifications of mammographic images using a deep convolutional neural network-based approach
Han et al. Breast cancer multi-classification from histopathological images with structured deep learning model
Ramos-Pollán et al. Discovering mammography-based machine learning classifiers for breast cancer diagnosis
Bilal et al. A quantum-optimized approach for breast cancer detection using SqueezeNet-SVM
Vijila Rani et al. Lung lesion classification scheme using optimization techniques and hybrid (KNN-SVM) classifier
Shiny Brain tumor segmentation and classification using optimized U-Net
Kiran et al. Advancing breast ultrasound diagnostics through hybrid deep learning models
Iqbal et al. AMIAC: adaptive medical image analyzes and classification, a robust self-learning framework
Kaur et al. An enhanced version of Harris Hawks optimization by dimension learning-based hunting for breast cancer detection
Islam Bhuiyan et al. Deep learning-based analysis of COVID-19 X-ray images: Incorporating clinical significance and assessing misinterpretation
Hameed et al. A deep learning approach for liver cancer detection in CT scans
Waqas et al. Brain tumor segmentation and surveillance with deep artificial neural networks
Semwal et al. A hybrid CNN-SVM model optimized with PSO for accurate and non-invasive brain tumor classification
Guerroudji et al. Efficient machine learning-based approach for brain tumor detection using the CAD system
Glory Precious et al. Deployment of a mobile application using a novel deep neural network and advanced pre-trained models for the identification of brain tumours
Lamba et al. Synergizing advanced algorithm of explainable artificial intelligence with hybrid model for enhanced brain tumor detection in healthcare
Kumar et al. Precise brain tumor classification from MRI images with hybrid recurrent neural network-bidirectional LSTM and humming bird optimization
Chen et al. Deep learning-aided 3D proxy-bridged region-growing framework for multi-organ segmentation
Reena Roy et al. An improved k-means clustering for segmentation of pancreatic tumor from CT images
Kumar et al. Prostate cancer classification with MRI using Taylor-Bird squirrel optimization based deep recurrent neural network
Potadar et al. Design and development of a deep learning model for brain abnormality detection using MRI
Jyothi et al. An attention 3DUNET and visual geometry group-19 based deep neural network for brain tumor segmentation and classification from MRI
Selvathi et al. Advanced computational intelligence techniques based computer aided diagnosis system for cervical cancer detection using pap smear images
Hussien et al. Explainable self-supervised learning for medical image diagnosis based on DINO V2 model and semantic search
Kumar et al. Enhancing brain tumor segmentation using Berkeley wavelet transformation and improved SVM