Enhancing the Diagnosis of Skin Cancer Using Active Learning and Transfer Learning Techniques

Abstract

Nowadays, skin cancer is regarded as one of the deadliest cancers that people can get. Skin cancer can be either benign or malignant, with melanoma being the most surprising of the two types. They include basal and melanoma. The topic of Sustainable Development Goal (SDG) 3: "Good Health and Well-Being" is relevant to this work. In particular, the goal of this manuscript is to lower the early mortality rate from non-communicable diseases (NCDs), such as cancer, by one-third through treatment and prevention. Treatment for melanoma cancer may benefit from early detection. Numerous systems in use today attest to the significant role that computer vision may play in medical image diagnosis. In this work, an offer methodology for classifying medical photos into benign or malignant tumors using machine learning is proposed. First, we organize and process the image data, and then we train and evaluate the model. The first step of the process is to collect image data from pre-made folders that have been separated into training and testing sets, as well as benign and malignant categories. File paths are combined into data frames for each category and set, which are subsequently labeled appropriately. Active Learning V3 + approach was employed at the inception.