scholarly journals Classification of skin diseases.

BMJ ◽  
1973 ◽  
Vol 3 (5875) ◽  
pp. 354-354
Author(s):  
E R Seiler
Keyword(s):  
Skin Diseases

Classification of large-scale image database of various skin diseases using deep learning

2021 ◽  
Author(s):  
Masaya Tanaka ◽  
Atsushi Saito ◽  
Kosuke Shido ◽  
Yasuhiro Fujisawa ◽  
Kenshi Yamasaki ◽  
...  
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Skin Diseases ◽  
Image Database

Classification of skin diseases based on improved MobileNetV2

2021 ◽  
Author(s):  
Yu Jia Cheng ◽  
Wei Lin ◽  
Yuan Zhen Liu ◽  
Lu Sun
Keyword(s):  
Skin Diseases

scholarly journals African Skin: Different Types, Needs and Diseases

2019 ◽  
pp. 1-13
Author(s):  
Olumayowa Abimbola Oninla ◽  
Samuel Olorunyomi Oninla ◽  
Bolaji Ibiesa Otike-Odibi ◽  
Mufutau Muphy Oripelaye ◽  
Fatai Olatunde Olanrewaju ◽  
...  
Keyword(s):  
Skin Color ◽  
Skin Diseases ◽  
Skin Pigmentation ◽  
Geographical Location ◽  
Skin Care ◽  
Skin Type ◽  
Protective Properties ◽  
Tropical Regions ◽  
The Right

Microscopic structures in the skin are basically the same in all races. Differences are found in histology and physiology of the skin resulting in different skin types, needs and prevailing skin diseases. Skin pigmentation (with the photo-protective properties), and the barrier function of the stratum corneum are the main differences between African and Caucasian skin. The geographic distribution of UV radiation (UVR) has a positive correlation with geographical location. The darker-skinned populations are closer to the equator where there are high amounts of UVR especially in the tropical regions of the world. African skin has the greatest variability in skin color. Africa has both white and dark skinned individuals with the darker-skinned populations being mostly around the equator.          Leslie Baumann introduced four parameters that more accurately characterized skin types than previous classification of dry, oily, normal and combination skin. These are dry or oily – D/O; sensitive or resistant – S/R; pigmented or non-pigmented – P/N, and wrinkled or unwrinkled skin – W/T. Combinations of these further produced sixteen skin phenotypes.  Dark skinned individuals often have the PT types while the light skinned mostly have the NW types. Skin needs basically depends on the type. Identifying the skin type is fundamental to providing the right skin care. According to Baumann, the fundamental elements of skin care are mild cleansing, hydrating (moisturization with humectants and emollients), replenishing (with lipids, ceramides and fatty acids) and skin protection (UV protection and increased humidity). Skin diseases are associated with skin type. Eczema is more typical in people with DS combinations while acne is associated with OS skin type (especially OSNT and OSPT). Prevalence of skin diseases varies within African communities from 35% to 87% with skin infections affecting 22-46% and eczemas 13-21% of patients in various studies.

Skin Cancer Classification with Deep Convolutional Neural Networks

2020 ◽  
Vol 10 (7) ◽  
pp. 1707-1713 ◽  
Author(s):  
Mingang Chen ◽  
Wenjie Chen ◽  
Wei Chen ◽  
Lizhi Cai ◽  
Gang Chai
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Skin Diseases ◽  
Survival Rates ◽  
Skin Lesions ◽  
Classification Model ◽  
Deep Convolutional Neural Networks ◽  
Image Preprocessing ◽  
Skin Cancers

Skin cancers are one of the most common cancers in the world. Early detections and treatments of skin cancers can greatly improve the survival rates of patients. In this paper, a skin lesions classification system is developed with deep convolutional neural networks of ResNet50, which may help dermatologists to recognize skin cancers earlier. We utilize the ResNet50 as a pre-trained model. Then, by transfer learning, it is trained on our skin lesions dataset. Image preprocessing and dataset balancing methods are used to increase the accuracy of the classification model. In classification of skin diseases, our model achieves an overall accuracy of 83.74% on nine-class skin lesions. The experimental results show an impressive effect of the ResNet50 model in finegrained skin lesions classification and skin cancers recognition.

"Zoological Classification" of Skin Diseases

1990 ◽  
Vol 29 (7) ◽  
pp. 531-532 ◽  
Author(s):  
Ernst H. Beutner ◽  
Gloria P. Beutner
Keyword(s):  
Skin Diseases ◽  
Zoological Classification
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