Self-Detection of Early Breast Cancer Application with Infrared Camera and Deep Learning

Abstract

Breast cancer is the most common cause of death in women around the world. A new tool has been adopted based on thermal imaging, deep convolutional networks, health applications on smartphones, and cloud computing for early detection of breast cancer. The development of the smart app included the use of Mastology Research with the Infrared Image DMR-IR database and the training of the modified version of deep convolutional neural network model inception V4 (MV4). In addition to designing the application in a graphical user interface and linking it with the AirDroid application to send thermal images from the smartphone to the cloud and to retrieve the suggestive diagnostic result from the cloud server to the smartphone. Moreover, to verify the proper operation of the app, a set of thermal images was sent from the smartphone to the cloud server from different distances and image acquisition procedures to verify the quality of the images. Four effects on the thermal image were applied: Blur, Shaken, Tilted, and Flipping were added to the images to verify the detection accuracy. After conducting repeated experiments, the classification results of early detection of breast cancer, generated from the MV4, illustrated high accuracy performance. The response time achieved after the successful transfer of diagnostic results from the smartphone to the cloud and back to the smartphone via the AirDroid application is six seconds. The results show that the quality of thermal images did not affect by different distances and methods except in one method when compressing thermal images by 5%, 15%, and 26%. The results indicate 1% as maximum detection accuracy when compressing thermal images by 5%, 15%, and 26%. In addition, the results indicate detection accuracy increased in Blurry images and Shaken images by 0.0002%, while diagnostic accuracy decreased to nearly 11% in Tilted images. Early detection of breast cancer using a thermal camera, deep convolutional neural network, cloud computing, and health applications of smartphones are valuable and reliable complementary tools for radiologists to reduce mortality rates.