Deep Learning Approaches for Prognosis of Automated Skin Disease
Skin problems are among the most common ailments on Earth. Despite its popularity, assessing it is not easy because of the complexities in skin tones, hair colors, and hairstyles. Skin disorders provide a significant public health risk across the globe. They become dangerous when they enter the inva...
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| Format: | Article |
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MDPI AG
2022-03-01
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| Series: | Life |
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| Online Access: | https://www.mdpi.com/2075-1729/12/3/426 |
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| author | Pravin R. Kshirsagar Hariprasath Manoharan S. Shitharth Abdulrhman M. Alshareef Nabeel Albishry Praveen Kumar Balachandran |
| author_facet | Pravin R. Kshirsagar Hariprasath Manoharan S. Shitharth Abdulrhman M. Alshareef Nabeel Albishry Praveen Kumar Balachandran |
| author_sort | Pravin R. Kshirsagar |
| collection | DOAJ |
| description | Skin problems are among the most common ailments on Earth. Despite its popularity, assessing it is not easy because of the complexities in skin tones, hair colors, and hairstyles. Skin disorders provide a significant public health risk across the globe. They become dangerous when they enter the invasive phase. Dermatological illnesses are a significant concern for the medical community. Because of increased pollution and poor diet, the number of individuals with skin disorders is on the rise at an alarming rate. People often overlook the early signs of skin illness. The current approach for diagnosing and treating skin conditions relies on a biopsy process examined and administered by physicians. Human assessment can be avoided with a hybrid technique, thus providing hopeful findings on time. Approaches to a thorough investigation indicate that deep learning methods might be used to construct frameworks capable of identifying diverse skin conditions. Skin and non-skin tissue must be distinguished to detect skin diseases. This research developed a skin disease classification system using MobileNetV2 and LSTM. For this system, accuracy in skin disease forecasting is the primary aim while ensuring excellent efficiency in storing complete state information for exact forecasts. |
| first_indexed | 2024-03-09T13:34:07Z |
| format | Article |
| id | doaj.art-36b70142c0044c258eae070138bbff28 |
| institution | Directory Open Access Journal |
| issn | 2075-1729 |
| language | English |
| last_indexed | 2024-03-09T13:34:07Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Life |
| spelling | doaj.art-36b70142c0044c258eae070138bbff282023-11-30T21:14:32ZengMDPI AGLife2075-17292022-03-0112342610.3390/life12030426Deep Learning Approaches for Prognosis of Automated Skin DiseasePravin R. Kshirsagar0Hariprasath Manoharan1S. Shitharth2Abdulrhman M. Alshareef3Nabeel Albishry4Praveen Kumar Balachandran5Department of Artificial Intelligence, G.H. Raisoni College of Engineering, Nagpur 412207, IndiaDepartment of Electronics and Communication Engineering, Panimalar Institute of Technology, Poonamallee, Chennai 600123, IndiaDepartment of Computer Science & Engineering, Kebri Dehar University, Kebri Dahar P.O. Box 250, EthiopiaDepartment of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi ArabiaDepartment of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi ArabiaDepartment of Electrical and Electronics Engineering, Vardhaman College of Engineering, Hyderabad 501218, IndiaSkin problems are among the most common ailments on Earth. Despite its popularity, assessing it is not easy because of the complexities in skin tones, hair colors, and hairstyles. Skin disorders provide a significant public health risk across the globe. They become dangerous when they enter the invasive phase. Dermatological illnesses are a significant concern for the medical community. Because of increased pollution and poor diet, the number of individuals with skin disorders is on the rise at an alarming rate. People often overlook the early signs of skin illness. The current approach for diagnosing and treating skin conditions relies on a biopsy process examined and administered by physicians. Human assessment can be avoided with a hybrid technique, thus providing hopeful findings on time. Approaches to a thorough investigation indicate that deep learning methods might be used to construct frameworks capable of identifying diverse skin conditions. Skin and non-skin tissue must be distinguished to detect skin diseases. This research developed a skin disease classification system using MobileNetV2 and LSTM. For this system, accuracy in skin disease forecasting is the primary aim while ensuring excellent efficiency in storing complete state information for exact forecasts.https://www.mdpi.com/2075-1729/12/3/426deep learninglearning algorithmsskin diseaseMobileNetV2LSTM |
| spellingShingle | Pravin R. Kshirsagar Hariprasath Manoharan S. Shitharth Abdulrhman M. Alshareef Nabeel Albishry Praveen Kumar Balachandran Deep Learning Approaches for Prognosis of Automated Skin Disease Life deep learning learning algorithms skin disease MobileNetV2 LSTM |
| title | Deep Learning Approaches for Prognosis of Automated Skin Disease |
| title_full | Deep Learning Approaches for Prognosis of Automated Skin Disease |
| title_fullStr | Deep Learning Approaches for Prognosis of Automated Skin Disease |
| title_full_unstemmed | Deep Learning Approaches for Prognosis of Automated Skin Disease |
| title_short | Deep Learning Approaches for Prognosis of Automated Skin Disease |
| title_sort | deep learning approaches for prognosis of automated skin disease |
| topic | deep learning learning algorithms skin disease MobileNetV2 LSTM |
| url | https://www.mdpi.com/2075-1729/12/3/426 |
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