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Towards Diagnosis of Autoimmune Blistering Skin Diseases Using Deep Neural Network
https://orcid.org/0000-0002-5108-1280
Abstract There are many skin disorders that affect human beings and symptoms of many skin diseases are common. Therefore, understanding the real differences between them is important for the correct diagnosis of dermatitis. Autoimmune blister skin diseases are uncommon skin diseases which come under heterogeneous group of disorders and happen when our immune system, which usually protects, attacks one’s own skin and mucous membrane mistakenly and cause blister (bulla) formation. The diagnosis of autoimmune blistering skin diseases is based on lesions examination and requires extensive, algorithmic array of immunologic investigations that are available in a few selected centers only and are cost intensive. Thus, a computerized system is needed to diagnose the diseases without such constraints. Computer-aided systems for detection are more precise, objective and reliable as opposed to diagnosis by expert. Deep neural networks, specifically convolutional neural networks, have been used for computer vision problems in several domains, have achieved the dermatologist-level accuracy in the classification of skin diseases. This paper provides the review on AIBD and its clinical features, existing tests for diagnosis, and need for early diagnosis of AIBD. Next, the techniques, challenges and shortcomings of traditional machine learning are highlighted. Then, the terminology and techniques used in the construction of convolutional neural network (CNN) and the existing use of deep neural networks, especially CNN, and transfer learning techniques for skin diseases are reviewed. Various approaches for segmentation using deep neural network are also pointed. At last, future works and conclusions about the classification of AIBD using deep learning techniques is discussed.
Towards Diagnosis of Autoimmune Blistering Skin Diseases Using Deep Neural Network
https://orcid.org/0000-0002-5108-1280
Abstract There are many skin disorders that affect human beings and symptoms of many skin diseases are common. Therefore, understanding the real differences between them is important for the correct diagnosis of dermatitis. Autoimmune blister skin diseases are uncommon skin diseases which come under heterogeneous group of disorders and happen when our immune system, which usually protects, attacks one’s own skin and mucous membrane mistakenly and cause blister (bulla) formation. The diagnosis of autoimmune blistering skin diseases is based on lesions examination and requires extensive, algorithmic array of immunologic investigations that are available in a few selected centers only and are cost intensive. Thus, a computerized system is needed to diagnose the diseases without such constraints. Computer-aided systems for detection are more precise, objective and reliable as opposed to diagnosis by expert. Deep neural networks, specifically convolutional neural networks, have been used for computer vision problems in several domains, have achieved the dermatologist-level accuracy in the classification of skin diseases. This paper provides the review on AIBD and its clinical features, existing tests for diagnosis, and need for early diagnosis of AIBD. Next, the techniques, challenges and shortcomings of traditional machine learning are highlighted. Then, the terminology and techniques used in the construction of convolutional neural network (CNN) and the existing use of deep neural networks, especially CNN, and transfer learning techniques for skin diseases are reviewed. Various approaches for segmentation using deep neural network are also pointed. At last, future works and conclusions about the classification of AIBD using deep learning techniques is discussed.
Towards Diagnosis of Autoimmune Blistering Skin Diseases Using Deep Neural Network
https://orcid.org/0000-0002-5108-1280
Abstract There are many skin disorders that affect human beings and symptoms of many skin diseases are common. Therefore, understanding the real differences between them is important for the correct diagnosis of dermatitis. Autoimmune blister skin diseases are uncommon skin diseases which come under heterogeneous group of disorders and happen when our immune system, which usually protects, attacks one’s own skin and mucous membrane mistakenly and cause blister (bulla) formation. The diagnosis of autoimmune blistering skin diseases is based on lesions examination and requires extensive, algorithmic array of immunologic investigations that are available in a few selected centers only and are cost intensive. Thus, a computerized system is needed to diagnose the diseases without such constraints. Computer-aided systems for detection are more precise, objective and reliable as opposed to diagnosis by expert. Deep neural networks, specifically convolutional neural networks, have been used for computer vision problems in several domains, have achieved the dermatologist-level accuracy in the classification of skin diseases. This paper provides the review on AIBD and its clinical features, existing tests for diagnosis, and need for early diagnosis of AIBD. Next, the techniques, challenges and shortcomings of traditional machine learning are highlighted. Then, the terminology and techniques used in the construction of convolutional neural network (CNN) and the existing use of deep neural networks, especially CNN, and transfer learning techniques for skin diseases are reviewed. Various approaches for segmentation using deep neural network are also pointed. At last, future works and conclusions about the classification of AIBD using deep learning techniques is discussed.
Towards Diagnosis of Autoimmune Blistering Skin Diseases Using Deep Neural Network
Singh, Manbir (author) / Singh, Maninder (author) / De, Dipankar (author) / Handa, Sanjeev (author) / Mahajan, Rahul (author) / Chatterjee, Debajyoti (author)
2023
Article (Journal)
Electronic Resource
English
Towards Diagnosis of Autoimmune Blistering Skin Diseases Using Deep Neural Network
| Springer Verlag | 2023
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