Two-layer deep feature fusion for detection of breast cancer using thermography images

Abstract

Author(s): Ankita Patra, Santi Kumari Behera, Nalini Kanta Barpanda and Prabira Kumar Sethy*

Breast cancer is one of the leading causes of death for women worldwide. Deep convolutional neural network-supported breast thermography is anticipated to contribute substantially to early detection and facilitate therapy at an early stage. This study aims to examine how several cutting-edge deep learning techniques with feature fusion behave when used to detect breast cancer. The effectiveness of the two-layer fusion of AlexNet, vgg16, and vgg19 for detecting breast cancer using thermal images is assessed. With feature fusion of fc6 and fc8, VGG16 outperformed AlexNet andVGG19 among the three CNN models in all three bi-layer fusion combinations, achieving an accuracy of 99.62.

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