Abstract
One of the most common diseases around the globe is dermatological problem. Despite being frequent, its diagnosis is very challenging because of the complexity of its skin tone, colour and hair existence. This research offers a method to automatically forecast the many types of skin illnesses using a variety of computer vision-based approaches (deep learning). With the many skin photos, the system uses deep learning techniques to train itself. One of the main goals of this approach is improving the accuracy with which skin illness can be predicted and vast majority of the population is healthier. The most common causes of skin disorders include allergies, bacteria, mycosis, viruses, etc. The rapid development of medical and laser technologies that are founded on photonics has made it possible to identify skin issues in a manner that is both timely and precise. Such diagnoses can only be made with expensive, specialized medical equipment. As a result, deep learning algorithms help diagnose skin problems before they become severe. The categorization of skin conditions relies heavily on extracting features. Deep learning algorithms have substantially reduced the requirement for labour-intensive manual operations such as data restoration and extraction of features for classifications.
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Goindi, S., Thakur, K., Kapoor, D.S. (2023). Skin Disease Classification and Detection by Deep Learning and Machine Learning Approaches. In: Mishra, A., Gupta, D., Chetty, G. (eds) Advances in IoT and Security with Computational Intelligence. ICAISA 2023. Lecture Notes in Networks and Systems, vol 755. Springer, Singapore. https://doi.org/10.1007/978-981-99-5085-0_16
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