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Title |
Breast Cancer Detection with Thermal Images and using Deep Learning |
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Author |
Amit Sarode and Vibha Bora |
| Citation |
Vol. 23 No. 8 pp. 91-94 |
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Abstract |
According to most experts and health workers, a living creature's body heat is little understood and crucial in the identification of disorders. Doctors in ancient medicine used wet mud or slurry clay to heal patients. When either of these progressed throughout the body, the area that dried up first was called the infected part. Today, thermal cameras that generate images with electromagnetic frequencies can be used to accomplish this. Thermography can detect swelling and clot areas that predict cancer without the need for harmful radiation and irritational touch. It has a significant benefit in medical testing because it can be utilized before any observable symptoms appear. In this work, machine learning (ML) is defined as statistical approaches that enable software systems to learn from data without having to be explicitly coded. By taking note of these heat scans of breasts and pinpointing suspected places where a doctor needs to conduct additional investigation, ML can assist in this endeavor. Thermal imaging is a more cost-effective alternative to other approaches that require specialized equipment, allowing machines to deliver a more convenient and effective approach to doctors. |
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Keywords |
Breast Cancer, Thermal images, Deep Learning, VGG16 and Inception V3. |
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