Thermal infrared imaging based breast cancer diagnosis using machine learning techniques

Abstract

The human’s temperature is little known and important to the diagnosis of diseases, according to most researchers and health workers.In ancient medicine, doctors may treat patients with wet mud or slurry clay. The part that would dry up first was considered the diseased part when either of these spread over the body. This can be done today with thermal cameras generating pictures with electromagnetic frequencies. Inflammation and blockage areas that predict cancer without radiation or touch may be detected by thermography. It can be used before any visible symptoms occur as a great advantage in medical testing. Machine learning (ML) is used in this paper as statistical techniques to give software programs the capacity to learn from information without being directly coded. ML can help to do so by learning these thermal scans and identifying suspected areas where a doctor needs to research more. Thermal photography is a comparatively better alternative to other methods that need sophisticated equipment, enabling machines to provide an easier and more effective approach to clinics and hospitals.