Skin is the largest body organ that is prone to the environment mostspecifically. Therefore the skin is susceptible to many damages, including burndamage. Burns can endanger life and are linked to high morbidity and m...Skin is the largest body organ that is prone to the environment mostspecifically. Therefore the skin is susceptible to many damages, including burndamage. Burns can endanger life and are linked to high morbidity and mortalityrates. Effective diagnosis with the help of accurate burn zone and wound depthevaluation is important for clinical efficacy. The following characteristics areassociated with the skin burn wound, such as healing, infection, painand stressand keloid formation. Tissue regeneration also takes a significant amount of timefor formation while considering skin healing after a burn injury. Deep neural networks can automatically assist in the extraction of features from a burn image. Inour approach to burn wound analysis and regeneration of the tissue of the skinburn wound, we use the Faster RCNN (Regional Convolutional Neural Network),which is based on their severity of the burn wound. The success rates of skin curefor burning injuries can be dramatically increased with the use of different skinreplacements. Our objective is to analyze different deep learning techniques thatmay help to analyze and classify burn wounds in a superficial, partial and complete thickness, while treating burn wounds more accurately. The application ofFaster RCNN effectively classifies wound without first degree, second and thirddegree confusion, thus providing a suitable solution to burning wounds. Theadvancement in the field of profound training offers an important path in the fieldof the processing and burning of trauma.展开更多
文摘Skin is the largest body organ that is prone to the environment mostspecifically. Therefore the skin is susceptible to many damages, including burndamage. Burns can endanger life and are linked to high morbidity and mortalityrates. Effective diagnosis with the help of accurate burn zone and wound depthevaluation is important for clinical efficacy. The following characteristics areassociated with the skin burn wound, such as healing, infection, painand stressand keloid formation. Tissue regeneration also takes a significant amount of timefor formation while considering skin healing after a burn injury. Deep neural networks can automatically assist in the extraction of features from a burn image. Inour approach to burn wound analysis and regeneration of the tissue of the skinburn wound, we use the Faster RCNN (Regional Convolutional Neural Network),which is based on their severity of the burn wound. The success rates of skin curefor burning injuries can be dramatically increased with the use of different skinreplacements. Our objective is to analyze different deep learning techniques thatmay help to analyze and classify burn wounds in a superficial, partial and complete thickness, while treating burn wounds more accurately. The application ofFaster RCNN effectively classifies wound without first degree, second and thirddegree confusion, thus providing a suitable solution to burning wounds. Theadvancement in the field of profound training offers an important path in the fieldof the processing and burning of trauma.
