Self-detection of early breast cancer application with infrared camera and deep learning
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...
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Main Authors: | , , , |
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Format: | Article |
Language: | English |
Published: |
MDPI
2021
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Subjects: | |
Online Access: | http://irep.iium.edu.my/93143/7/93143_Self-detection%20of%20early%20breast%20cancer%20application.pdf http://irep.iium.edu.my/93143/ https://www.mdpi.com/2079-9292/10/20/2538/htm |
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Summary: | 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. |
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