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


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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Awards Nomination

Editors List

  • Prof. Elhadi Miskeen

    Obstetrics and Gynaecology Faculty of Medicine, University of Bisha, Saudi Arabia

  • Ahmed Hussien Alshewered

    University of Basrah College of Medicine, Iraq

  • Sudhakar Tummala

    Department of Electronics and Communication Engineering SRM University – AP, Andhra Pradesh




  • Alphonse Laya

    Supervisor of Biochemistry Lab and PhD. students of Faculty of Science, Department of Chemistry and Department of Chemis


  • Fava Maria Giovanna


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