There are more than 200 different types of cancer. Melanoma is the most deadly type of skin cancer out of 200. Clinical screening is the first step in the melanoma diagnostic process, which is then followed by dermoscopic analysis and histological investigation.
Skin cancer with melanoma has a good chance of being cured if it is discovered in its early stages. Visual inspection of the afflicted area of skin is the initial stage in the diagnosis of melanoma skin cancer.
Dermatologists use a high-speed camera to capture dermatoscopic images of skin lesions, which have an accuracy of 65–80% in the diagnosis of melanoma without any additional technical assistance. The overall accuracy of melanoma diagnosis increased to 75-84% with additional visual assessment by cancer treatment specialists and dermatoscopic pictures.
In order to categorise skin cancer using photographs of skin lesions, the project intends to develop an automated classification system based on image processing techniques.
The dermatologist removes a portion of the skin lesion during the skin biopsy and studies it under a microscope. Obtaining a dermatologist appointment and receiving a biopsy report currently takes around a week or more.
By offering the prediction model, the intends to reduce the existing gap to a matter of days. The method analyses photos of outlier lesions to classify nine different forms of skin cancer using convolutional neural networks (CNN). This gap closing has the potential to positively affect millions of individuals.
Supporting initiatives to lower skin cancer-related deaths is the main objective. Utilising cutting-edge picture categorization technology for human welfare is the project's main driving force. Machine learning and deep learning techniques for computer vision have advanced significantly and are scalable across domains.