Turkish Journal of Electrical Engineering and Computer Sciences




This paper proposed an accurate and fully automated breast cancer early screening system called the "Breast Cancer-Caps". The capsule network is used in this approach for the cancer detection in breast utilizing the thermal infrared images for the first time. This capsule network is trained with the help of Dynamic as well as Static breast thermal images dataset consisting of left, right, frontal views along with a new multiview thermal images. These multiview breast thermal images are fabricated by concatenating the conventional left, frontal and right view breast thermal images. The other current and popular deep transfer learning models such as Visual Geometry Group 19 (VGG 19), Residual Network 50 (ResNet50V2) and InceptionV3 network are also trained with the aid of same Static and Dynamic breast thermal images augmented dataset for comparing the performance of these models with the proposed system. The "Breast Cancer-Caps" system tends to delivers the best testing and validation accuracies as compared to their other deep transfer learning models. This proposed system delivers an encouraging testing accuracy of more than 99% utilizing the multiview breast thermal images as input over the Dynamic breast thermal images testing dataset. Whereas the testing accuracies of 95%, 94% and 89% are achieved by the VGG 19, ResNet50V2, InceptionV3 models respectively over the Dynamic breast thermal images testing dataset utilizing the same multiview breast thermal images as input.


Thermal images, breast cancer, capsule network, ResNet50V2, InceptionV3, VGG 19, Static, Dynamic, multiview

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