AI Doc Helper
DOI:
https://doi.org/10.17010/ijcs/2022/v7/i4/172377Keywords:
Artificial Intelligence
, Cancer, Detection.Manuscript Received
, May 27, 2022, Revised, June 15, Accepted, June 20, 2022. Date of Publication, August 5, 2022.Abstract
For numerous times, numerous people have failed due to undetected conditions. Early discovery of these conditions at the micro bracket stage can be useful for furnishing proper treatment of the cases at an early stage and could have saved a lot of lives. A lot of exploration is being done to describe these conditions at the foremost. Thus, a computer-backed or Artificial Intelligence approach for detecting conditions at the early stage is being proposed, which makes use of machine, literacy and deep literacy algorithms for detecting conditions. This system will describe all general conditions similar to different types of cancer, malaria, diabetic retinopathy, etc. AI-Doc Helper is being proposed as there's no system available that detects all these general conditions.Downloads
Downloads
Published
How to Cite
Issue
Section
References
P. Bagga and R. Hans, “Applications of mobile agents in healthcare domain: A literature survey,†Int. J. Grid Distribution Comput., vol. 8, no. 5, pp. 55–72, 2015. [Online]. Available: http://article.nadiapub.com/IJGDC/vol8_no5/5.pdf
Z. Y. Zhuang, L. Churilov, F. Burstein, and K. Sikaris, “Combining data mining and case-based reasoning for intelligent decision support for pathology ordering by general practitioners,†Eur. J. Oper. Res., vol. 195, no. 3, pp. 662–675, 2009, doi: 10.1016/j.ejor.2007.11.003.
R. S. Dick, E. B. Steen, and D. E. Detmer, The computer-based patient record: An essential technology for health care. National Academies Press-Washington DC, USA, 1997.
J. E. Wennberg, “Dealing with medical practice variations: A proposal for action,†Health Affairs, vol. 3, no. 2, pp. 6–32, 1984. https://pubmed.ncbi.nlm.nih.gov/6432667/
W.S.A. Smellie, D. Wilson, C. A. M. McNulty, J. J., W. Irvine, P. C. Dore, G. Handley, M. J. Galloway, G. A.. Spickett, D. I. Finnigan, D. A. Bareford, M. A. Greig, and J. Richards, “Best practice in primary care pathology: Review 1,†J. Clin. Pathology, vol. 58, no. 10, pp. 1016–1024, 2005, doi: 10.1136/jcp.2004.025049.
M. Daniels and S. A. Schroeder, “Variation among physicians in the use of laboratory tests II. Relation to clinical productivity and outcomes of care,†Med. Care, vol. 15, no. 6, , pp. 482–487, 1977, doi: 10.1097/00005650-197706000-00004.
P. J. Stuart, S. Crooks, and M. Porton, “An interventional program for diagnostic testing in the emergency department,†Med. J., vol. 177, no.3, pp. 131–134, 2002, doi: 10.5694/j.1326-5377.2002.tb04697.x.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,†2015. [Online]. Available: arxiv.org/abs/1409.1556.
R. Casanova, S. Saldana, E. Y. Chew, R. P. Danis, C. M. Greven, and W. T. Ambrosius, “Application of random forests methods to diabetic retinopathy classification analyses,†Plos One, vol. 9, no. 6, p. e98587, 2014, doi: 10.1371/journal.pone.0098587.
“Malaria cells image dataset,†[Online]. Available: https://www.kaggle.com/iarunava/cell-images-for-detecting-malaria
https://scholar.cu.edu.eg/?q=fahmy/pages/dataset
“Brain Tumor Classification (MRI),†[Online]. Available: https://www.kaggle.com/sartajbhuvaji/brain-tumor-classification-mri
“Chronic Kidney Disease Dataset,†[Online]. Available: [https://archive.ics.uci.edu/ml/datasets/Chronic_Kidney_Disease
