Melanoma, the deadliest form of skin cancer, is becoming more common
every year, according to the American Cancer Society. As a result of the artifacts, low
contrast, and similarity to other lesions, such as moles and scars on the skin, diagnosing
skin cancer from the lesions might be difficult. Skin cancer can be diagnosed using a
variety of techniques, including dermatology, dermoscopic examination, biopsy and
histological testing. Even though the vast majority of skin cancers are non-cancerous
and do not constitute a threat to survival, certain more malignant tumors can be fatal if
not detected and treated on time. In reality, it is not feasible for every patient to have a
dermatologist do a complete examination of his or her skin at every visit to the doctor's
office or clinic. To solve this challenge, numerous investigations are being conducted
to provide computer-aided diagnoses. In this work, skin cancer can be predicted from
an image of the skin using deep learning techniques such as convolutional neural
networks. The accuracy and loss functions of the model are used to evaluate its overall
performance. The mobile app is created to detect skin cancer using the developed
model. As soon as the images have been submitted, the app can communicate with the
user about their progress.
Keywords: Convolutional Neural Network, Deep Learning Algorithm, Melanoma, Web Development.
