Approach of Image Processing in Diagnosis and Medication of Fungal Infections in Pet Animals
Keywords:
Disease Diagnosis Treatment Animal Pet Image Dataset Machine Learning Image processingAbstract
The focus of this paper is to design and develop the Artificial Intelligence and Machine Learning based system which detects the fungal infection on a pet animal (especially Dog and Cat), and provide the treatment for it. It also provides the causes, Symptoms, Prevention for it. The proposed system integrates on Machine Learning algorithm trained on disease dataset, it first detects the species of pet animal by capturing the image of fungal infection of pet by using camera or by uploading the image in the system from the file explorer, and then provide the diagnosis about the fungal disease and its medication and treatment also. Although our system will verify the scan image for determining the diseases in canine. Diagnosing the Fungal Infection on pet animal quickly and accurately has the economic effectiveness.
Downloads
References
Seyedmojtaba Seyedmousavi, Sandra de M G Bosco, Sybren de Hoog, Frank Ebel, Daniel Elad, Renata R Gomes, Ilse D Jacobsen, Henrik E Jensen, An Martel, Bernard Mignon, Frank Pasmans, Elena Piecková, Anderson Messias Rodrigues, Karuna Singh, Vania A Vicente, Gudrun Wibbelt, Nathan P Wiederhold, Jacques Guillot, “Fungal infections in animals: a patchwork of different situations”, Medical Mycology, Volume 56, Issue suppl_1, 1 April 2018, Pages S165–S187
Moriah Morrison, Veterinary Health Centre http://vhc.missouri.edu/small-animal-hospital/small-a nimal-internal-medicine/diseases-and-treatments/fun gal-disease//// Access on 08/05/2020.
Dat Tran, ‘’Building a real time object recognition app with tensorflow and opencv’’, 22/06/2017, towardsdatascience.
‘’GUI Programming in Python’’, Python
https://wiki.python.org/moin/GuiProgramming//// Access on 09/05/2020
N. Singh and P. Kaur, "Comprehensive review of techniques used to detect skin lesion," 2017 2nd International Conference for Convergence in Technology (I2CT), Mumbai, 2017, pp. 100-105, doi: 10.1109/I2CT.2017.8226102.
Niko Quiskamp, Sören Wildenhain, Thomas Schmidts, Peter Mayser, Frank Runkel, and Martin Fiebich: ‘’Image-Processing Scheme to Detect Superficial Fungal Infections of the Skin’’, 2015, Hindwai
Keyvan Asefpour Vakilian, Jafar Massah: ‘’ An artificial neural network approach to identify fungal diseases of cucumber (Cucumis sativus L.) plants using digital image processing’’, 25/03/2013, Archives of Phytopathology and Plant Protection.
Yogesh Joshi: ‘’ Digital Image Processing Techniques for Detecting Plant or Animal Diseases’’, 2015, Grin. [9] L. Yuan, Z. Qu, Y. Zhao, H. Zhang and Q. Nian, "A convolutional neural network based on TensorFlow for face recognition," 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chongqing, 2017, pp. 525-529, doi: 10.1109/IAEAC.2017.8054070. (Basic Book/Monograph Online Sources) J. K. Author. (year, month, day). Title (edition) [Type of medium]. Volume(issue). Available: http://www.(URL) [10]Abhinav Datich: ‘’Practical Computer Vision’’, 02/2018, PACKT.
Tahir, Muhammad. (2019). Fungus Detection Using Computer Vision and Machine Learning Techniques. Research Gate
Abdellatif Abdelfattah: ‘’ Image Classification using Deep Neural Networks — A beginner friendly approach using TensorFlow’’, 28/07/2017, Medium.com
