Detection of skin cancer image based on convolutional neural network model and website application
Manye Dong · International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2021) · 2022
In contemporary society, convolutional neural networks (CNNs) are used mainly for image processing, classification, segmentation, including detecting skin cancer. However, many current CNN models used to analyze skin cancer heavily rely on enough information apart from photos of skin lesions. This need hinders the users from diagnosing themselves. To tackle this issue, the paper proposed a new but simple CNN model. It can be utilized to detect and differentiate benign and malignant skin cancer on the patients’ end using image classification. Deep convolutional neural networks, such as CNN, demonstrate the potential to be properly trained and making predictions based on the given training set. In this paper, we show classification of skin cancer using a single CNN model, trained end-to-end from real skin lesion images directly. It only uses pixels and disease labels as inputs. We used the malignant vs benign skin cancer dataset generated by Claudio Fancon. Images are provided by the public ISIC (International Skin Image Collaboration) 2018 Skin Lesion Dataset. Furthermore, we resize the original images to a desirable resolution that is ready to be used for training. Then, we build a multiple-layer CNN model and train it on all the data. Finally, we fit the model to a local user interface for future use. The proposed model is found to be successful and promising, achieving testing results with 82% accuracy on the test set. This could significantly reduce human mistakes in the skin cancer diagnosis process. It also obtains an average loss of 0.45, which is a relatively low figure on the test set. The proposed system, therefore, is relatively reliable and robust when detecting potential skin cancer.