Automatic breast cancer detection using inception V3 in thermography

Al-Hussaini, Mohammed Abdulla Salim and Habaebi, Mohamed Hadi and Gunawan, Teddy Surya and Islam, Md. Rafiqul and Hameed, Shihab A. (2021) Automatic breast cancer detection using inception V3 in thermography. In: 2021 8th International Conference on Computer and Communication Engineering (ICCCE), 22-23 June 2021, Kuala Lumpur, Malaysia.

	PDF - Published Version Restricted to Registered users only Download (2MB) | Request a copy
	PDF (schedule) - Supplemental Material Restricted to Registered users only Download (392kB) | Request a copy
	PDF (SCOPUS) - Published Version Restricted to Registered users only Download (235kB) | Request a copy

Abstract

Thermography is a non-invasive, passive imaging technique that is widely used in the medical profession, particularly for breast tumor diagnosis. This research paper suggests the examination of breast thermography through the utilize of Deep convolutional neural network inception v3 including training several times and fine- tuning learning rate. It is hoped that such strategies will enable the development of highly accurate thermography-based diagnostic methods. The proposed technique is training inception v3: Continuous training, training after shutting down MATLAB and after shutting down computer, and the average accuracy of the classification was obtained from epoch 3, 5 and 6, which are 98.104%, 98.712 and 97.816%, respectively. The use best value of learning rate allows the correlation between the variables accuracy, and this is critical because it aids in the selection of the proper variables to be applied in the Deep convolutional neural network's construction. The proposed learning rate has achieved highest accuracy between 1e-3 and 2.5e-3 are 97.816% and 99.928% respectively in identifying breast cancer.

Item Type:	Conference or Workshop Item (Invited Papers)
Additional Information:	6727/90606
Uncontrolled Keywords:	Thermography, Breast Cancer, Inception V3, Deep Convolutional Neural Network, Learning Rate.
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101 Telecommunication. Including telegraphy, radio, radar, television
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering Kulliyyah of Engineering > Department of Electrical and Computer Engineering
Depositing User:	Dr. Mohamed Hadi Habaebi
Date Deposited:	21 Jul 2021 16:26
Last Modified:	20 Sep 2021 14:20
URI:	http://irep.iium.edu.my/id/eprint/90606

Actions (login required)

View Item