FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4377096547> ?p ?o ?g. }

• W4377096547 endingPage "1875" @default.
• W4377096547 startingPage "1869" @default.
• W4377096547 abstract "ABSTRACTABSTRACTOsteosarcoma and osteochondroma are the most common malignant and benign bone tumours. In order to distinguish osteosarcoma from osteochondroma, divide-specific areas and distinguish different lesions. Herein, a computer-aided medical diagnosis was developed for the first time based on existing mask regional convolutional neural network (Mask R-CNN) to detect them in X-ray radiographs used for their initial screening. Mask R-CNN was trained using an amplified training and validation sets consisting of 378 and 52 ×-ray radiographs and propose the removing heterogeneous module and the de-overlapping module in the post-processing process to obtain the predictive segmentation mask. Two tests were used to predict, which were composed of 84 images from 72 patients involved in the training set but different from the images used within, and 61 images from 35 new patients who were not included in the training set. The mean Average Precision, mean Precision, mean Recall and mean Intersection Over Union were introduced metrics to evaluate the performance, which were 0.9486, 0.9211, 0.9545 and 0.6603 for test sets 1, and 0.9290, 0.8690, 0.9481 and 0.6222 for test 2. The results demonstrated that the developed method was convincing in distinguishing the type and detecting the region of osteosarcoma and osteochondroma compared with manual work.KEYWORDS: Bone tumoursX-ray radiographscomputer-aid medical diagnosisdeep learningconvolutional neural networks Disclosure statementNo potential conflict of interest was reported by the authors.Supplementary materialSupplemental data for this article can be accessed online at https://doi.org/10.1080/21681163.2023.2196577.Additional informationFundingContract grant sponsor: This study was supported by grants from by graduate scientific research and innovation foundation of Chongqing, China (CYS19055). The dataset used in the experiment was approved by Ethics Committee of the Affiliated Hospital of Chongqing University (CZLS2021185-A)." @default.
• W4377096547 created "2023-05-20" @default.
• W4377096547 creator A5015102287 @default.
• W4377096547 creator A5044296818 @default.
• W4377096547 creator A5073938410 @default.
• W4377096547 creator A5086128666 @default.
• W4377096547 creator A5091717095 @default.
• W4377096547 date "2023-05-19" @default.
• W4377096547 modified "2023-09-25" @default.
• W4377096547 title "The Development of Mask R-CNN to Detect Osteosarcoma and Oste-ochondroma in X-ray Radiographs" @default.
• W4377096547 cites W1542046697 @default.
• W4377096547 cites W2019209162 @default.
• W4377096547 cites W2023392165 @default.
• W4377096547 cites W2040749787 @default.
• W4377096547 cites W2122390343 @default.
• W4377096547 cites W2141597812 @default.
• W4377096547 cites W2403768253 @default.
• W4377096547 cites W2913330188 @default.
• W4377096547 cites W2963150697 @default.
• W4377096547 cites W2965338984 @default.
• W4377096547 cites W3107396352 @default.
• W4377096547 cites W3113359589 @default.
• W4377096547 cites W3151680346 @default.
• W4377096547 cites W3187928299 @default.
• W4377096547 cites W3214763121 @default.
• W4377096547 cites W4210370254 @default.
• W4377096547 cites W4247069559 @default.
• W4377096547 cites W4280510429 @default.
• W4377096547 cites W4289258880 @default.
• W4377096547 cites W98651880 @default.
• W4377096547 doi "https://doi.org/10.1080/21681163.2023.2196577" @default.
• W4377096547 hasPublicationYear "2023" @default.
• W4377096547 type Work @default.
• W4377096547 citedByCount "0" @default.
• W4377096547 crossrefType "journal-article" @default.
• W4377096547 hasAuthorship W4377096547A5015102287 @default.
• W4377096547 hasAuthorship W4377096547A5044296818 @default.
• W4377096547 hasAuthorship W4377096547A5073938410 @default.
• W4377096547 hasAuthorship W4377096547A5086128666 @default.
• W4377096547 hasAuthorship W4377096547A5091717095 @default.
• W4377096547 hasConcept C108583219 @default.
• W4377096547 hasConcept C126838900 @default.
• W4377096547 hasConcept C142724271 @default.
• W4377096547 hasConcept C153180895 @default.
• W4377096547 hasConcept C154945302 @default.
• W4377096547 hasConcept C169903167 @default.
• W4377096547 hasConcept C177264268 @default.
• W4377096547 hasConcept C199360897 @default.
• W4377096547 hasConcept C205649164 @default.
• W4377096547 hasConcept C2777760704 @default.
• W4377096547 hasConcept C2778529550 @default.
• W4377096547 hasConcept C36454342 @default.
• W4377096547 hasConcept C41008148 @default.
• W4377096547 hasConcept C58640448 @default.
• W4377096547 hasConcept C64543145 @default.
• W4377096547 hasConcept C71924100 @default.
• W4377096547 hasConcept C81363708 @default.
• W4377096547 hasConcept C81669768 @default.
• W4377096547 hasConcept C89600930 @default.
• W4377096547 hasConceptScore W4377096547C108583219 @default.
• W4377096547 hasConceptScore W4377096547C126838900 @default.
• W4377096547 hasConceptScore W4377096547C142724271 @default.
• W4377096547 hasConceptScore W4377096547C153180895 @default.
• W4377096547 hasConceptScore W4377096547C154945302 @default.
• W4377096547 hasConceptScore W4377096547C169903167 @default.
• W4377096547 hasConceptScore W4377096547C177264268 @default.
• W4377096547 hasConceptScore W4377096547C199360897 @default.
• W4377096547 hasConceptScore W4377096547C205649164 @default.
• W4377096547 hasConceptScore W4377096547C2777760704 @default.
• W4377096547 hasConceptScore W4377096547C2778529550 @default.
• W4377096547 hasConceptScore W4377096547C36454342 @default.
• W4377096547 hasConceptScore W4377096547C41008148 @default.
• W4377096547 hasConceptScore W4377096547C58640448 @default.
• W4377096547 hasConceptScore W4377096547C64543145 @default.
• W4377096547 hasConceptScore W4377096547C71924100 @default.
• W4377096547 hasConceptScore W4377096547C81363708 @default.
• W4377096547 hasConceptScore W4377096547C81669768 @default.
• W4377096547 hasConceptScore W4377096547C89600930 @default.
• W4377096547 hasFunder F4320321135 @default.
• W4377096547 hasIssue "5" @default.
• W4377096547 hasLocation W43770965471 @default.
• W4377096547 hasOpenAccess W4377096547 @default.
• W4377096547 hasPrimaryLocation W43770965471 @default.
• W4377096547 hasRelatedWork W2731899572 @default.
• W4377096547 hasRelatedWork W2790662084 @default.
• W4377096547 hasRelatedWork W2999805992 @default.
• W4377096547 hasRelatedWork W3116150086 @default.
• W4377096547 hasRelatedWork W3133861977 @default.
• W4377096547 hasRelatedWork W3166467183 @default.
• W4377096547 hasRelatedWork W4200173597 @default.
• W4377096547 hasRelatedWork W4291897433 @default.
• W4377096547 hasRelatedWork W4312417841 @default.
• W4377096547 hasRelatedWork W4321369474 @default.
• W4377096547 hasVolume "11" @default.
• W4377096547 isParatext "false" @default.
• W4377096547 isRetracted "false" @default.
• W4377096547 workType "article" @default.