Fan Liu¹, Jianwei Yan², Wantao Wang², Jian Liu^{2, *}, Junying Li³, Alan Yang⁴

CMC-Computers, Materials & Continua, Vol.62, No.2, pp. 801-816, 2020, DOI:10.32604/cmc.2020.07039

Abstract Skin lesion recognition is an important challenge in the medical field. In this paper, we have implemented an intelligent classification system based on convolutional neural network. First of all, this system can classify whether the input image is a dermascopic image with an accuracy of 99%. And then diagnose the dermoscopic image and the non-skin mirror image separately. Due to the limitation of the data, we can only realize the recognition of vitiligo by non-skin mirror. We propose a vitiligo recognition based on the probability average of three structurally identical CNN models. The method is more efficient and robust than… More >

Frederick H. Silver^1,*, Ruchit G. Shah², Dominick Benedetto³

Molecular & Cellular Biomechanics, Vol.16, Suppl.2, pp. 73-73, 2019, DOI:10.32604/mcb.2019.08150

Abstract Previous literature reports suggest that tissue stiffness is a predictor of cancer and metastatic behavior of lesions. We have used optical coherence tomography and vibrational analysis (VOCT) to characterize normal skin, scar, a verrucous carcinoma (a squamous cell carcinoma subtype), a basal cell carcinoma and benign skin lesions non-invasively and non-destructively. The results suggest that epidermal thickening and increased keratin and basal cell production occur in malignant lesions and lead to increases in surface hills and valleys as well as subsequent increases in epidermal stiffness values. Increased stiffness of the epidermis is a result of increased keratin and basal cell… More >