OPTIMIZING SKIN CANCER DETECTION IN THE USA HEALTHCARE SYSTEM USING DEEP LEARNING AND CNNS
Authors:   Department of Management Science and Quantitative Methods, Gannon University, Erie, PA, USA, Nasiruddin Md , Hider Mohammad Abir ,   Master of Science in Business Analytics, Grand Canyon University, Phoenix, AZ, USA, Akter Rabeya ,   Master of Science in information technology. Washington University of Science and Technology, Alexandria, VA, USA, Alam Shah ,   Master of Science in Information Technology, Washington University of Science and Technology, Alexandria, VA, USA, Mohaimin MD Rashed ,   MBA in Business Analytics, Gannon University, Erie, PA, USA, Khan MD Tushar ,   Master of Science in Business Analytics, Trine University, Angola, IN, USA, Sayeed Abdullah AL ,   Master of Business Administration in Project Management, Central Michigan University, Mt Pleasant, MI, USA, jui Afrin hoque ,   Management Sciences and Quantitative Methods, Gannon University, Eria, PA, USA
Published: December 2024

Skin cancer is among the most prevalent cancers in the USA, with millions of new cases reported each year. The two main types of skin cancer include aggressive, life-threatening melanoma and less lethal, though potentially very morbid if left unattended, non-melanoma types: basal cell carcinoma and squamous cell carcinoma. The chief aim of this research project is to devise, curate, and propose a deep-learning CNN methodology for skin cancer detection in the USA. The dataset for the current research project was retrieved from the Kaggle website, particularly, The ISIC 2016 Skin Cancer Dataset contained dermoscopic images that were used for skin cancer classification. In this dataset, there were 1271 images of two classes of skin cancer, namely Malignant and Benign. These images were then gathered from the ISIC archive. The dataset was then divided into a training set consisting of 1022 images and a test set consisting of 249 images. The CNN proposed for this work is a deep-learning architecture designed to address skin cancer detection through dermoscopic images. The model follows a sequential architecture with multiple layers dedicated to the extraction of hierarchical features from input images. To assess the performance of the CNN algorithm for skin cancer detection, several proven metrics are utilized, namely, accuracy, precision, recall, and F1-Score. The model obtained a very high precision, recall, and F1-score over all classes, with a general accuracy of 94% for this multi-class problem. This model was very good, both in precision since it correctly identifies the actual positive cases and in recall, where it does not have false positives. The developed proposed CNN model for skin cancer detection has great potential to support human clinical decision-making in all dermatology. This developed model automates the various analyses of dermoscopy images, hence acting as just an adjunct tool for active dermatologists, which shall enable fast and accurate skin lesion assay. Results have shown that this CNN can easily be integrated into diagnosis workflows in normal dermatological practice to offer a second opinion or even a pre-screening tool for dermatologists.