Skin Tumours

Computer-aided systems for skin lesion diagnosis is a growing area of research. Recently, researchers have shown an increasing interest in developing computer-aided diagnosis systems. This paper aims to review, synthesize and evaluate the quality of evidence for the diagnostic accuracy of computer-aided systems. This study discusses the papers published in the last five years in ScienceDirect, IEEE, and SpringerLink databases. It includes 53 articles using traditional machine learning methods and 49 articles using deep learning methods. The studies are compared based on their contributions, the methods used and the achieved results. The work identified the main challenges of evaluating skin lesion segmentation and classification methods such as small datasets, ad hoc image selection and racial bias.

Abstract

Iron overload is known to occur due to different factors including genetic disorders. It has been shown that iron overload predisposes humans to an increased risk of cancer. However, the mechanism by which iron overload enhances chemically induced carcinogenesis is not known. In this report, for the first time it is shown that iron overload acts as a tumour initiator. Female albino Swiss mice were given iron dextran 1 mg/mouse per day intramuscularly for 15 days and croton oil 0.5 mg in 200 mL acetone/mouse topically twice a week for 30 weeks. During this period, the animals were observed for tumour incidence. There were significantly higher yields of tumours in those animals receiving both iron and croton oil. However, the group of animals treated only with acetone, iron, croton oil and 7,12-dimethylben-z(a)anthracene (DMBA) alone did not develop any tumours during 30 weeks of observation. Further, croton oil-mediated induction in cutaneous lipid peroxidation (LPO) level was higher in the iron-overload group. The results of this study suggest that oxidative stress generated by iron overload is responsible for croton oilmediated skin carcinogenesis.