Advanced Convolutional Neural Network Model to Identify Melanoma Skin Cancer

Today, a significant number of people are diagnosed with cancer of the skin. Melanoma and other types of skin cancer make up the vast majority of cases. The early detection and treatment of this cancer is the most wise and reliable technique for avoiding the development of the condition. The purpose of this research report is to offer a hybrid neural network model that can identify melanoma and non-melanoma skin malignancies with a higher degree of precision. This categorization model utilizes ACNN to integrate three different models. Both the training and evaluation of the model make use of two different benchmark datasets. In this investigation, the EfficientNetV2 model was used. This model showed better accuracy. The Inception layer of the model has been constructed, and the model’s overall accuracy is 98%. The model has a ROC value of 99, which stands for receiver operating characteristic. The model received a score of 97.5% on the F1 scale. As a main diagnostic procedure, this strategy is beneficial for patients as well as for clinicians, and it has the potential to reduce the death rate among skin cancer patients.