Skin Lesion Detection and Classification

Abstract

Skin cancer has an overall mortality rate of 0.6 to 0.7% and accounts for 5.8% of cases worldwide (1.6% for melanoma). Globocan 2018 is challenged by Pakistan's Punjab Cancer Registry (PCR), which records an increased incidence. According to PCR, skin cancer is eighth and ninth most common in both males and females in 2017. It ranks in the top eight for Karachi according to the Karachi Cancer Registry (KCR). According to Dow University of Health Sciences (DUHS), non-melanoma skin cancer is the sixth most common type of the disease, which indicates a notable increase in Karachi. This study identifies key contributors among universities, research institutions, and cities and objectively assesses Pakistan's skin cancer research, examining output, types, and focuses. Additionally, it intends to identify the main institutions and cities that have made significant contributions to this field of research. The research presents a sophisticated computerized diagnostic method that apply Inception V3 architecture. This method achieves an impressive accuracy, when tested on the HAM10000 dataset, highlighting its effectiveness in identifying and diagnosing skin ailments. The hybrid machine learning approach, when applied to a dataset of 3672 categorized pictures, produces a diagnosis accuracy of 99.80% on testing while achieved validation accuracy of 84.27%. This shows promise for improving the categorization of skin cancer and potentially leading to advancements in diagnosis, treatment, and mortality rates.