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Deep Learning Approach for Dermototis Identification
Deep Learning Approach for Dermototis Identification
The advancements in Computer Vision with Deep Learning are playing an essential role in aiding Healthcare Organizations to offer better patient care while reducing costs and improving efficiencies. The medical industry has seen a sharp growth since the past few decades and it has benefited thousands of living beings. Skin being the largest part of human beings, it has never been given the proper care nor importance, hence widely resulting in the ignorance of skin infections or cancers. Despite the diagnosis of several such diseases being detected at the later or critical stages, there is a little to no scope of detection where the affected people can identify the symptoms immediately and could reach a physician. In this paper, we present the development of a model using deep learning approach to identify the specific dermatitis and a mobile application that functions in a way to detect the disease when a picture of the symptomatic part is taken and uploaded. This scheme assists the medical professionals and patients during pandamic situations like COVID 19. This application is user friendly and very much useful for the people living in rural areas and hilly area where it takes long time and expenditure to reach hospitals.
Microsoft Academic Graph classification: Business Artificial intelligence business.industry Rural area Internet privacy User Friendly Health care Scope (project management) Disease Deep learning Ignorance media_common.quotation_subject media_common Identification (information)
Microsoft Academic Graph classification: Business Artificial intelligence business.industry Rural area Internet privacy User Friendly Health care Scope (project management) Disease Deep learning Ignorance media_common.quotation_subject media_common Identification (information)
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).0 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).0 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
Popularity provided by BIP!
The advancements in Computer Vision with Deep Learning are playing an essential role in aiding Healthcare Organizations to offer better patient care while reducing costs and improving efficiencies. The medical industry has seen a sharp growth since the past few decades and it has benefited thousands of living beings. Skin being the largest part of human beings, it has never been given the proper care nor importance, hence widely resulting in the ignorance of skin infections or cancers. Despite the diagnosis of several such diseases being detected at the later or critical stages, there is a little to no scope of detection where the affected people can identify the symptoms immediately and could reach a physician. In this paper, we present the development of a model using deep learning approach to identify the specific dermatitis and a mobile application that functions in a way to detect the disease when a picture of the symptomatic part is taken and uploaded. This scheme assists the medical professionals and patients during pandamic situations like COVID 19. This application is user friendly and very much useful for the people living in rural areas and hilly area where it takes long time and expenditure to reach hospitals.