Role of Artificial Intelligence for Skin Cancer Detection

2021 ◽  
pp. 141-174
Author(s):  
Piyush Kumar ◽  
Rishi Chauhan ◽  
Achyut Shankar ◽  
Thompson Stephan
Keyword(s):  
Artificial Intelligence ◽  
Skin Cancer ◽  
Cancer Detection ◽  
Skin Cancer Detection

scholarly journals Artificial Intelligence for Skin Cancer Detection: A Scoping Review (Preprint)

10.2196/22934 ◽  
2020 ◽  
Author(s):  
Abdulrahman Takiddin ◽  
Jens Schneider ◽  
Yin Yang ◽  
Alaa Abd-Alrazaq ◽  
Mowafa Househ
Keyword(s):  
Artificial Intelligence ◽  
Skin Cancer ◽  
Cancer Detection ◽  
Scoping Review ◽  
Skin Cancer Detection

scholarly journals Mobile Artificial Intelligence Applications for Skin Cancer Diagnostics: Preferences and Concerns of Digital Natives (Preprint)

2020 ◽  
Author(s):  
Sarah Haggenmüller ◽  
Eva Krieghoff-Henning ◽  
Tanja Jutzi ◽  
Nicole Trapp ◽  
Lennard Kiehl ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Clinical Practice ◽  
Skin Cancer ◽  
Cancer Detection ◽  
Mobile Applications ◽  
Positive Attitude ◽  
Age Groups ◽  
Digital Natives ◽  
Cancer Diagnostics ◽  
Skin Cancer Detection

BACKGROUND Artificial Intelligence (AI) has shown potential to improve diagnostics of various diseases and especially early skin cancer detection. What is missing is the bridge from AI technology to clear application scenarios in clinical practice as well as added value for patients. Translation of AI-based diagnostic tools can only be successful if they are accepted by potential users. Young adults as digital natives may offer the greatest potential for successful implementation into clinical practice, while at the same time representing the future generation of skin cancer screening participants. OBJECTIVE We conducted an anonymous online survey to examine how and to what extent individuals are willing to accept AI-based mobile applications for skin cancer diagnostics. In this way, we evaluated the preferences as well as the relative influence of concerns with a special focus on younger age groups. METHODS We recruited respondents below 35 years of age through the social media channels Facebook, LinkedIn and Xing. Descriptive analysis and statistical tests were performed to evaluate participants’ attitudes towards mobile applications for skin examination. An adaptive choice-based conjoint (ACBC) was integrated to assess respondents’ preferences. Potential concerns were evaluated using maximum difference scaling (MaxDiff). RESULTS 728 respondents were included in the analysis. About 66.5% expressed a positive attitude towards the use of AI-based applications. In particular participants residing in big cities or small towns and individuals that were familiar with the use of health or tracking apps were significantly more open towards mobile diagnostic systems. Hierarchical Bayes estimation (HB) of the preferences of participants with positive attitude (n=484) revealed that the use of mobile applications as an assistance system was preferred. Respondents ruled out app versions with an accuracy of 65 percent or less, applications using data storage without encryption as well as systems that did not deliver background information about the decision-making. However, participants did not mind their data being used anonymously for research purposes, nor did they object to the inclusion of clinical patient information into the decision-making process. MaxDiff analysis for the negative-minded participant group (n=244) outlined that data security, insufficient trust in the app, as well as the lack of personal interaction represented the dominant concerns with respect to app use. CONCLUSIONS The majority of potential future users below 35 years of age was ready to accept AI-based diagnostic solutions for early skin cancer detection. However, for translation into clinical practice, participants’ demand for increased transparency and explainability of AI-based tools seems to be critical. Altogether, digital natives expressed similar preferences and concerns when compared to results obtained by previous studies that included other age groups.

Automated Skin Cancer Detection Web-Based Application and Review of Some Image Classification Algorithm

2020 ◽  
Author(s):  
Siddhesh Bhojane ◽  
Krishna Shrestha ◽  
Sanghmitra Bharadwaj ◽  
Ritul Yadav ◽  
Fenil Ribinwala ◽  
...  
Keyword(s):  
Skin Cancer ◽  
Image Classification ◽  
Cancer Detection ◽  
Classification Algorithm ◽  
Web Based ◽  
Skin Cancer Detection

scholarly journals Epoch interrogation for skin cancer detection using convolutional neural network models

2021 ◽  
Vol 1074 (1) ◽  
pp. 012025
Author(s):  
A Poornima ◽  
M Shyamala Devi ◽  
M Sumithra ◽  
Mullaguri Venkata Bharath ◽  
Swathi ◽  
...  
Keyword(s):  
Neural Network ◽  
Skin Cancer ◽  
Convolutional Neural Network ◽  
Cancer Detection ◽  
Network Models ◽  
Neural Network Models ◽  
Skin Cancer Detection
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