Melanoma Skin Cancer Detection Using Deep learning, Neural Network and Classical Machine Learning Techniques

Abstract

Melanoma is one of the deadliest types of skin cancer and requires an early check-up for survival. Though earlier diagnosis was primarily dependent on dermatological checks and histopathological reports, automation of the same may be highly time-efficient. With that interest, in the following paper, we propose a hybrid approach which attempts to utilize deep learning with some classical machine learning methods to automate melanoma detection. The feature extraction and classification were done using CNN because it is the most efficient in processing image data. Besides, SVM and KNN were used as comparative models. A very well-preprocessed dataset of images was used to see how well models could work in terms of accuracy, precision, and recall. We observe strong over-performance by CNNs compared to the more traditional methods and an ensemble of models that yield a higher confidence of diagnostics. The technique has much promise for supporting clinicians with quicker and more precise diagnosing of melanoma, thus enabling better patient care. Keywords: Melanoma detection, dermatological, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Convolutional Neural Networks (CNNs).