[go: up one dir, main page]

ALI - Google Patents

EARLY IDENTIFICATION OF COLORECTAL CANCER USING CONVOLUTIONAL NEURAL NETWORK (DEEP LEARNING)

ALI

View PDF
Document ID
14201708602375033796
Author
ALI M

External Links

Snippet

Artificial intelligence (AI) has contributed immensely in the field of medicine, especially in the Oncological sector. The use of different networks such as deep learning for the detection of cancer has proven to be very effective in performance. However, this study focuses on …
Continue reading at docs.neu.edu.tr (PDF) (other versions)

Classifications

    • 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
    • G06T7/0014—Biomedical image inspection using an image reference approach
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by the preceding groups
    • G01N33/48—Investigating or analysing materials by specific methods not covered by the preceding groups biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay
    • G01N33/574—Immunoassay; Biospecific binding assay for cancer
    • G01N33/57407—Specifically defined cancers
    • 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
    • G06T2207/20112—Image segmentation 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
    • 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
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00—Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image
    • G06T3/0031—Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image for topological mapping of a higher dimensional structure on a lower dimensional surface
    • G06T3/0037—Reshaping or unfolding a 3D tree structure onto a 2D plane
    • 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
    • G06K9/00127—Acquiring and recognising microscopic objects, e.g. biological cells and cellular parts

Similar Documents

Publication Publication Date Title
Zhang et al. Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment
Fujisawa et al. Deep‐learning‐based, computer‐aided classifier developed with a small dataset of clinical images surpasses board‐certified dermatologists in skin tumour diagnosis
Xu et al. Using transfer learning on whole slide images to predict tumor mutational burden in bladder cancer patients
Kanavati et al. Deep learning models for gastric signet ring cell carcinoma classification in whole slide images
Chen et al. A radiomics signature in preoperative predicting degree of tumor differentiation in patients with non–small cell lung cancer
Hu et al. Non-invasive evaluation for benign and malignant subcentimeter pulmonary ground-glass nodules (≤ 1 cm) based on CT texture analysis
Azar et al. Automated system for colon cancer detection and segmentation based on deep learning techniques
Lu et al. Uncontrolled confounders may lead to false or overvalued radiomics signature: a proof of concept using survival analysis in a multicenter cohort of kidney cancer
Kumar et al. Mammogram image segmentation using SUSAN corner detection
Singh et al. Segeir-net: A robust histopathology image analysis framework for accurate breast cancer classification
Qi et al. CT radiomics-based biomarkers can predict response to immunotherapy in hepatocellular carcinoma
Deng et al. Machine learning models combining computed tomography semantic features and selected clinical variables for accurate prediction of the pathological grade of bladder cancer
Modi et al. Role of artificial intelligence in detecting colonic polyps during intestinal endoscopy
Li et al. Multi-size deep learning based preoperative computed tomography signature for prognosis prediction of colorectal cancer
Xin et al. Application of artificial intelligence in endoscopic gastrointestinal tumors
Singh et al. A comprehensive assessment of artificial intelligence applications for cancer diagnosis
Aftab et al. AI in oncology: transforming cancer detection through machine learning and deep learning applications
Zhu et al. Advancements in the application of artificial intelligence in the field of colorectal cancer
Zheng et al. KD_ConvNeXt: knowledge distillation-based image classification of lung tumor surgical specimen sections
Jiang et al. Computational tumor stroma reaction evaluation led to novel prognosis-associated fibrosis and molecular signature discoveries in high-grade serous ovarian carcinoma
Su et al. Accurate measurement of colorectal polyps using computer-aided analysis
Monabbati et al. Automated analysis of computerized morphological features of cell clusters associated with malignancy on bile duct brushing whole slide images
Faria et al. Cancer Detec-Lung Cancer Diagnosis Support System: First Insights.
Shang et al. Deep learning and radiomics for gastric cancer serosal invasion: automated segmentation and multi-machine learning from two centers
ALI EARLY IDENTIFICATION OF COLORECTAL CANCER USING CONVOLUTIONAL NEURAL NETWORK (DEEP LEARNING)