Artificial Intelligence Based Skin Classification Using GMM

Monisha, M.; Suresh, A.; Rashmi, M. R.

doi:10.1007/s10916-018-1112-5

Cited by 16 publications

(6 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The use of A.I‐based technologies is a rather recent milestone, mostly—and logically—first devoted to medical concerns, 24‐28 where classic imaging techniques (standardized photographs, videos, optical microscopic images, etc) are used. A few works, medical‐oriented, attempted to use smartphone's images (not selfies) as primary sources of A.I‐based analysis 29‐31 .…”

Section: Discussionmentioning

confidence: 99%

“…As it is well established that facial expression alters the appearance of some facial signs, 24‐29 1,023 women from the three ethnics (Table 3), of comparable age, were asked to take 15 selfie images in a row with their own smartphone‘s frontal camera (Resolution ≥5Megapixels). Hence, these images were taken under indoor conditions, by adopting different subtle variations from the most neutral expression, that is slight smile, slight pout or disapproval, etc at their own convenience.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Developing an Artificial Intelligence (A.I)‐based descriptor of facial appearance that fits with the assessments of makeup experts

Flament

Zhang²,

Zhi³

et al. 2021

Skin Research and Technology

View full text Add to dashboard Cite

Objective To develop an A.I‐based automatic descriptor that detects and grades, from selfie pictures, 23 facial signs, hairs included, as a help to making‐up procedures. Material and Methods The selfie images taken in very different conditions by 3326 women and men were used to create (90% of dataset) and validate (10% of dataset) a new algorithm architecture to appraise and grade 23 different facial signs such as lips, nose, eye color, eyebrows, eyelashes, and hair color as defined by makeup artists. Each selfie image was annotated by 12 experts and defined references to train Artificial Intelligence (A.I)‐based algorithm. Results As some the 23 signs present a continuous or discontinuous feature, these were analyzed by two different statistical approaches. The results provided by the automatic descriptor system were not only in good agreement with the expert's assessments but were even found of a better precision and reproducibility. This automatic descriptor system has proven a good and robust accuracy despite the very variable conditions in the acquisition of selfie pictures. Conclusion Such automatic descriptor system seems providing a valuable help in making‐up procedures and may extend to other activities such as Skincare or Haircare. As such it should allow large investigations to better evaluate the consumers’ needs of esthetical improvements.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Developing an Artificial Intelligence (A.I)‐based descriptor of facial appearance that fits with the assessments of makeup experts

Flament

Zhang²,

Zhi³

et al. 2021

Skin Research and Technology

View full text Add to dashboard Cite

show abstract

“…Automated detection of skin lesion using images has extended beyond melanoma to encompass pigmentary skin lesions, non-melanocytic skin cancers, psoriasis, skin rash, and onychomycosis among other skin diseases. 26,[91][92][93][94][95]…”

Section: Artificial Intelligence Applications In Dermatological Conditionsmentioning

confidence: 99%

Artificial intelligence in dermatology and healthcare: An overview

Pai

2021

IJDVL

View full text Add to dashboard Cite

Many aspects of our life are affected by technology. One of the most discussed advancements of modern technologies is artificial intelligence. It involves computational methods which in some way mimic the human thought process. Just like other branches, the medical field also has come under the ambit of artificial intelligence. Almost every field in medicine has been touched by its effect in one way or the other. Prominent among them are medical diagnosis, medical statistics, robotics, and human biology. Medical imaging is one of the foremost specialties with artificial intelligence applications, wherein deep learning methods like artificial neural networks are commonly used. artificial intelligence application in dermatology was initially restricted to the analysis of melanoma and pigmentary skin lesions, has now expanded and covers many dermatoses. Though the applications of artificial intelligenceare ever increasing, large data requirements, interpretation of data and ethical concerns are some of its limitations in the present day.

show abstract

“…Given the lack of adequate laboratory facilities in most countries, diagnosis, isolation, and treatment are delayed, which can lead to further infection and death. For these reasons, researchers have developed various methods for the AI-assisted diagnosis of skin diseases (Alenezi et al, 2023;Bhatt et al, 2022;Elashiri et al, 2022;Monisha et al, 2018;Qian et al, 2022;Wei et al, 2022;Xin et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Detection of Monkeypox Among Different Pox Diseases with Different Pre-Trained Deep Learning Models

ÇELİK

İni̇k

2023

Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi

View full text Add to dashboard Cite

Monkeypox is a viral disease that has recently rapidly spread. Experts have trouble diagnosing the disease because it is similar to other smallpox diseases. For this reason, researchers are working on artificial intelligence-based computer vision systems for the diagnosis of monkeypox to make it easier for experts, but a professional dataset has not yet been created. Instead, studies have been carried out on datasets obtained by collecting informal images from the Internet. The accuracy of state-of-the-art deep learning models on these datasets is unknown. Therefore, in this study, monkeypox disease was detected in cowpox, smallpox, and chickenpox diseases using the pre-trained deep learning models VGG-19, VGG-16, MobileNet V2, GoogLeNet, and EfficientNet-B0. In experimental studies on the original and augmented datasets, MobileNet V2 achieved the highest classification accuracy of 99.25% on the augmented dataset. In contrast, the VGG-19 model achieved the highest classification accuracy with 78.82% of the original data. Considering these results, the shallow model yielded better results for the datasets with fewer images. When the amount of data increased, the success of deep networks was better because the weights of the deep models were updated at the desired level.

show abstract

Artificial Intelligence Based Skin Classification Using GMM

Cited by 16 publications

References 17 publications

Developing an Artificial Intelligence (A.I)‐based descriptor of facial appearance that fits with the assessments of makeup experts

Developing an Artificial Intelligence (A.I)‐based descriptor of facial appearance that fits with the assessments of makeup experts

Artificial intelligence in dermatology and healthcare: An overview

Detection of Monkeypox Among Different Pox Diseases with Different Pre-Trained Deep Learning Models

Contact Info

Product

Resources

About