Litcius/Paper detail

Context based healthcare informatics system to detect gallstones using deep learning methods

M Yildirim, H Kurum, H Sun, H Tang, S Jiang, L Zeng, E Chen, T Zhou, E Shaffer, P Portincasa, A Moschetta, G Palasciano, K Heaton, F Braddon, R Mountford, A Hughes, P Emmett, C Chen, C Lin, C Kao, G Choy, O Khalilzadeh, M Michalski, S Do, A Samir, O Pianykh, Srj Van, R Cohen, Y Eldar, Srj Van, L Demi, R Anantharaman, M Velazquez, Y Lee, M Haq, A Irtaza, N Nida, M Shah, L Zubair, Y Hsieh, C Chin, C Wei, I Chen, P Yeh, R Tseng, G Sha, J Wu, B Yu, N Sokolova, M Taschwer, S Sarny, D Putzgruberadamitsch, K Schoeffmann, J Kim, J Kwon, H Kim, H Lee, Y Ro, C Chen, S Ruan, C Lin, C Hung, L Chen, T Xie, X Wang, C Wang, M Lin, C Chen, C Lai, E Mohedano, K Mcguinness, G Healy, O'connor Ne, A Smeaton, A Salvador, A Kanakatte, A Ramaswamy, J Gubbi, A Ghose, B Purushothaman, R Jain, A Sutradhar, A Dash, S Das, A Obaid, A Turki, H Bellaaj, M Ksontini, X Qifang, Y Guoqing, L Pin, S Basu, M Gupta, P Rana, P Gupta, C Arora, Y Tao, Z Zongyang, Z Jun, C Xinghua

2022International Journal of Advanced Technology and Engineering Exploration11 citationsDOIOpen Access PDF

Abstract

Gallbladder stone ailment is the utmost prevalent gastrointestinal illness that necessitates hospitalization, with a projected 800,000 cholecystectomies in the United States each year

Topics & Concepts

GallstonesContext (archaeology)Health informaticsHealth careComputer scienceInformaticsDeep learningHealthcare systemArtificial intelligenceMedicineInternal medicineEngineeringGeographyPolitical scienceElectrical engineeringArchaeologyLawSmart Systems and Machine Learning
Context based healthcare informatics system to detect gallstones using deep learning methods | Litcius