Early cancers detection by means of smartphone with semi-supervised learning that may minimize False Negative Rate (FNR)
We can broaden the scope and thus the utility of our paper beyond breast cancer, e.g. skin cancers and whatever is detectable by local change of heat generation over time through the modern days convenient and powerful multiple spectral image processing deep learning (DL) algorithm by Smartphone’s. We have proposed a novel Smartphone day & dual IR night (e.g. from the day visible – 0.4 – 0.75 microns – and to the night near infrared – 0.78 – 3 microns – VISNIR, medium wave infrared – 3-5 microns – MW and long wave infrared – 8-12 microns – LW). Artificial Neural Network emulate Human Visual System Cortex 17 layer by layer so-called Deep Learning Algorithm loaded in Smartphone which may be able to spot early and advanced signs of tumors formations in the all skin related region. Furthermore, we adopt the available data from Nat. Lib. of Medicine (loc. National Institute of Health, cf. Fig.1 ) data-guided semi-unsupervised learning at our algorithm at min. False Negative Rate). As long as we do not change the cancer diagnosis standard, e.g. taking samples and growing on a Petri dish, etc. and letting the physicians make the final decision, we do not have legal liability, but moral obligations.