Automated Skin Cancer Detection System Using Deep Transfer Learning

One of the most prevalent cancers, both melanoma and non-melanoma, causes hundreds of thousands of deaths globally each year. Skin cell growth that isn't normal is how it shows up. Recovery chances are significantly increased by early diagnosis. Furthermore, it might reduce the need for or use of chemical, radiographic, or surgical therapies altogether. A dermatoscope is used in the traditional method of visual inspection by a dermatologist or primary care physician in order to detect skin-related diseases. Patients who exhibit early signs of skin cancer are referred for a biopsy and histopathological examination in order to confirm the diagnosis and determine the most appropriate course of treatment. Recent developments in deep convolutional neural networks (CNNs) have led to automated skin cancer classification with excellent performance and accuracy comparable to dermatologists. These advancements haven't, however, yet produced a widely used and clinically reliable method for the identification of skin cancer. As a result, medical expenses can be decreased. Dermoscopy, which examines the general size, shape, and color characteristics of skin lesions, is the first step in the diagnosis of skin cancer. Suspected lesions then undergo additional sampling and laboratory testing for confirmation. Because deep learning artificial intelligence has become more popular, image-based diagnosis has advanced significantly in recent years.