Medical Internet of Things using Deep Learning Techniques for Skin Cancer Detection
Vivek Veeraiah, G K Ravikaumar, R. Kalpana, K. Sreenivasulu, Yashpal Singh, Surendra Kumar Shukla · 2022
It has always been difficult to diagnose skin cancer using eye inspection and manual study of photographs of skin lesions. It might take a lot of time and effort to manually examine skin lesions to look for melanoma. Skin cancer, especially melanoma, is one of the deadliest conditions. It is more challenging to identify and diagnose different skin lesions due to the similarities between them in colour skin imaging, such as carcinoma and nevi. Many deep learning models and machine learning methods have evolved for the interpretation of medical pictures, particularly the images of skin lesions, as a result of technological innovation and the rapid rise in processing resources. Early skin cancer identification and classification enable patients to receive appropriate diagnosis and care. This research presents a novel "deep learning framework" for skin lesion identification in skin images that is supported by the "internet of health and things (IoHT)" and is based on transfer learning. In order to obtain pertinent data and logical arguments from many sources, this research work has concentrated on taking the secondary qualitative technique into consideration.