Skin Cancer Classification using Deep Learning based Convolutional Neural Network Model

Dermatological disorders are among the most significant medical issues of the twenty-first century due to their difficult and unpredictable human interpretation and extraordinarily complex and expensive diagnosis. For determining the possibility of recovery from life-threatening conditions like melanoma, early detection is essential. Early identification of skin cancer is vital to effective treatment and better outcomes. Although specialists are capable of accurately diagnosing cancer, their limited supply forces the creation of automated techniques. Through this, lives will be saved and patients' monetary and medical burdens will be reduced. Artificial intelligence (AI) and machine learning can be very useful in this scenario. The foundations of machine learning and how it might improve skin cancer early recognition are explained in this article. Therefore, the present study describes a totally automated method of dermatological cancer recognition from lesion images, in opposition to traditional medical personnel-based detection. In this work, three stages i.e. data gathering & augmentation, model creation, and prediction have been used. To develop an improved structure and obtain an accuracy of 82%, image processing technologies were combined with convolutional neural network technologies.