Authors :
Prathamesh Ghan; Santosh Ghorpade; Sharon Mariam Philip; Charansigh Ingle; Dr Aniruddha S Rumale
Volume/Issue :
Volume 7 - 2022, Issue 5 - May
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3O5graF
DOI :
https://doi.org/10.5281/zenodo.6629930
Abstract :
In global terms health issues are considered a
2
nd priority compared to a lot of other issues in day-to-day
life. As health issues are playing second fiddle, Skin
diseases are regarded as not too important compared to
other major league diseases such as Cancer, HIV, Stroke
etc. As skin diseases are becoming more and more common
in day-to-day life because of numerous reasons such as
Poor Diet, Oily Fast Food, Pollution in the surrounding,
Stressful working environment, etc. as it can also be
genetic condition (hereditary) in one’s nature. The aim is
to understand the research that has goneinto the detection
of skin diseases using modern technologies and methods to
speed up the process and to achieve better accuracy of the
detection and classification of various skin diseases.
Keywords :
Identification of Skin diseases , Deep learning , Solutions, Convolution Neural Network (CNN).
In global terms health issues are considered a
2
nd priority compared to a lot of other issues in day-to-day
life. As health issues are playing second fiddle, Skin
diseases are regarded as not too important compared to
other major league diseases such as Cancer, HIV, Stroke
etc. As skin diseases are becoming more and more common
in day-to-day life because of numerous reasons such as
Poor Diet, Oily Fast Food, Pollution in the surrounding,
Stressful working environment, etc. as it can also be
genetic condition (hereditary) in one’s nature. The aim is
to understand the research that has goneinto the detection
of skin diseases using modern technologies and methods to
speed up the process and to achieve better accuracy of the
detection and classification of various skin diseases.
Keywords :
Identification of Skin diseases , Deep learning , Solutions, Convolution Neural Network (CNN).
