Application of Artificial Intelligence in Medicine

Szerzők

  • Tatjana Muratović University of Donja Gorica

Kulcsszavak:

Machine learning, Deep Learning, Blood cells, Medicine, Neural networks

Absztrakt

Blood is one of the most essential parts of the human body, and it comprises of the Red Blood Cells, White Blood Cells, and Platelets. Complete blood count characterizes the condition of well-being. Hence, segmentation and identification of blood cells is very important. Up to this day, many hospitals and health centers still use the old conventional method which involves manual counting of blood cells using haemocytometer along with other laboratory equipment's and chemical compounds, which is a time-consuming and tedious task. In this work, the author presents a machine learning approach for automatic identification and counting of three types of blood cells using Detectron2, a popular PyTorch based modular computer vision model library. Detectron2 detector has been trained on public blood cell detection data hosted at Roboflow. Overall, the computer-aided system of detection and counting enables us to count blood cells from smear images in less than a second, which is useful for practical applications.

Hivatkozások

[1] Bojana Dalbelo Bašić, Marko Čupić, and Jan Šnajder. Umjetne neuronske mreže. Zagreb: Fakultet elektrotehnike i računarstva, 2008.
[2] Dan Claudiu Cireşan, Ueli Meier, Luca Maria Gambardella, and Jürgen Schmidhuber. Deep, big, simple neural nets for handwritten digit recognition. Neural computation, 22(12), 2010.
[3] Nilkanth Mukund Deshpande, Shilpa Gite, and Rajanikanth Aluvalu. A review of microscopic analysis of blood cells for disease detection with ai perspective. PeerJ Computer Science, 7, 2021.
[4] Issam El Naqa, Ruijiang Li, and Martin J Murphy. Machine learning in radiation oncology: theory and applications. Springer, 2015.
[5] Yanming Guo, Yu Liu, Ard Oerlemans, Songyang Lao, Song Wu, and Michael S Lew. Deep learning for visual understanding: A review. Neurocomputing, 187, 2016.
[6] Neha Gupta et al. Artificial neural network. Network and Complex Systems, 3(1), 2013.
[7] Jianxing He, Sally L Baxter, Jie Xu, Jiming Xu, Xingtao Zhou, and Kang Zhang. The practical implementation of artificial intelligence technologies in medicine. Nature medicine, 25(1), 2019.
[8] Werner Horn. Ai in medicine on its way from knowledge-intensive to data-intensive systems. Artificial Intelligence in Medicine, 23(1), 2001.
[9] Richard Lippmann. An introduction to computing with neural nets. IEEE Assp magazine, 4(2), 1987.
[10] Lorenzo Putzu and Cecilia Di Ruberto. White blood cells identification and classification from leukemic blood image. In International Work-Conference on Bioinformatics and Biomedical Engineering. Copicentro Editorial, 2013.
[11] Alan M Turing. Computing machinery and intelligence. In Parsing the turing test. Springer, 2009

##submission.downloads##

Megjelent

2023-11-28

Folyóirat szám

Évf. 5 szám 1 (2023): Bánki Közlemények/Bánki Reports Autumn (2023)

Rovat

Technical Informatics (Műszaki Informatika)

License

Bánki Közlemények is loyal to open access for academic work. All the original articles and review papers published in this journal are free to access immediately from the date of publication. We don’t charge any fees for any reader to download articles and reviews for their own scholarly use.

The Bánki Közlemények also operates under the Creative Commons Licence CC-BY-NC-ND. This allows for the reproduction of articles, free of charge, for non-commercial use only and with the appropriate citation information. All authors publishing with the Bánki Közlemények accept these as the terms of publication.