Malignancy is the second most common reason of death in most countries. Diagnosis of thyroid cancers is one of the most difficult medical problems. Therefore, it important to use diagnosing methods that bring about quick results, are less costly, and
prevent errors based on personal factors. An example of such new methods is the use of automated spectral imaging, which gives more information about the structure of tissue, through the formation of fingerprint histological diagnosis used in the automated machine.
This study included 25 cancer cases and 26 specimens of non-malignant thyroid or normal. The credibility of method is reflected in the high rates of correct diagnosis in cancer samples (TP) = 92% and the negative result in sections of non-malignant
(FN) = 96%. This method represents the use of automation technologies and artificial intelligence to facilitate and speed up the diagnosis of cancers and can be used to diagnose other organs and other tissues.
Inasmuch as for incrimination of occurrence dermal malignant melanoma and difficulties of the clinical and histological diagnosis, so it is needed to search about method, giving fast diagnosis and self of high credibility, so the automatic spectral i
maging was for the histological slides of dermal melanoma, where use of the morphological information is complete with the local information in establishing of the tissue print, through education of the machine (machine learning). 31 Cutaneous biopsies have been studied, of their 11 malignant melanomas, 16 benign lesions, and 4 normal skin. It has done spectral imaging for Histological sections stained routinely during the evaluation in digital camera pick up (CCD Camera) on luminous spectroscope, his luminous wavelengths ranges from 390-700 nanometer. the automated classifying results showed in the acquaintance on the malignant cells in tumor tissues in a ratio of ranged between 65-82%, these are very promising results. method of the non-supported classification revealed better results. in the future, we must complete classification of all types of other tissues, and build data base characterized each type of disease in various tissues. in order to use it as supportive factor in the accurate diagnosis and correct.
