The week's pick
Random Articles
Reseach Article
Detection of Hepatomegaly by Automated-Diagnosis Method based on Artificial Neural Network
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 176 - Number 15 |
| Year of Publication: 2020 |
| Authors: Bratati Roy, Taskin Noor Turna, Md. Ebrahim Hossain, Mst. Alema Khatun |
10.5120/ijca2020920087
|
Bratati Roy, Taskin Noor Turna, Md. Ebrahim Hossain, Mst. Alema Khatun . Detection of Hepatomegaly by Automated-Diagnosis Method based on Artificial Neural Network. International Journal of Computer Applications. 176, 15 ( Apr 2020), 32-38. DOI=10.5120/ijca2020920087
Abstract
Diagnosis sonography (ultrasonography) is an ultrasound-based diagnostic imaging technique which is used for visualizing internal body structures including tendons, muscles, joints, vessels and internal organs for possible pathology. In Bangladesh, there is no formal training for the sonographer, that’s why sometimes there is a great possibility to make mistakes to find out the exact disease. Sonography is done manually. Sometimes the disease suggested by doctor and the diagnosis report that is done by the sonographer are not the same, for this reason the patient may be confused about his or her actual disease. To solve all these problems an automatic diagnosis tool is needed to get the required result of the required patient. Here, proposed an artificial neural network based model for automated diagnosis of hepatomegaly from ultrasound images to get an automated result which is appropriate for anyone.
References
- Ultrasonography. Retrieved date 18/10/18 Retrieved from https://www.fda.gov/radiation-emitting-products/medical-imaging/ultrasound-imaging.
- Dimtris K. Lakovidis, Eystratios G. Keramids, DimtrisMaroulis, “Fuzzy Local Binary patterns For Ultrasound Texture Characterization ”,3rd European Support Framework-PENED 2003 (grant no. 03-ED-662),2003.
- J.Yazbek, K. S. Raju, J. Ben-NaGI, T. Holland, K. Hillby, D. Jurkovic,”Accuracy of Ultrasound Subjective ‘Pattern recognition’ For The Diagnosis Of Borderline Ovarian Tumors”, published online in Wiley Interscience,DOI:10.1002/uog.4002,2007.
- Eko Supriyanto, Laikhin Wee, Too Yuen Min, “Ultrasonic Marker Pattern Recognition And Measurement Using Artificial Neural Network”, 9th WSEAS International Conference On Signal Processing, ISSN:1790-5117, ISBN:978-954-92600-4-5,2005.
- Jose De Jesus Rubio, Carlos Aviles, Raymundo Coello, Jose Francisco Cruz, Hector Rivero, “Pattern Recognition of Eye Movement”, IEEE. 2009.
- Jiri Blahuta, Tomas Soukup, Petr Cermak,’’The Image Recognition Of Brain-stem Ultrasound Images With Using a Neural Network Based on PCA’’, Issue 2,Volume 5,International Journal Of Applied Mathematics and Informatics,2011.
- Sameh Ghwanmeh, Adel Mohammad, Ali Al-Ibrahim,” Innovative Artificial Neural Networks-Based Decision Support System for Heart Diseases Diagnosis”, Journal of Intelligent Learning Systems and Applications, August 2013.
- Ultrasound. Retrieved date 18/10/18. Retrieved from https://www.medicalnewstoday.com/articles/245491#uses
- Artificial Neural Network. Retrieved date 18/10/18 Retrievedfromhttps://en.wikipedia.org/wiki/Artificial_neural_network
- Canny Edge Detection. Retrieve date 20/10/18. Retrieved fromhttp://fourier.eng.hmc.edu/e161/lectures/canny/node1.html.
Index Terms
Keywords