A Deep Neural Network Approach to Detect and Classify Skin Cancer (International Journal of Innovative Research in Computer Science & Technology, 2022)
Among all cancers, skin cancer is one of  the serious disease which effects major population across  the globe. If the detection and diagnosis would not happen  in early stages it can be spread to other body parts rapidly  from sunlight because the tissues and skin cells get effected  when exposed to sunlight. Although there are many  systems available with the medical industry but this  research is proposed to improve the performance of  existing system by employing deep learning feature of  Artificial Intelligence (AI) using Convolution Neural  Networks (CNN), where Convolution Neural Networks  (CNN) has been implemented using 3x3 and 5x5 matrix  along with Graphical User Interface (GUI) generation so  to provide better user experience (UX). This research  accomplished the desired parameters with values to  achieve 80.55% accuracy and 0.63% loss while training  and testing the model. The research has been supported  with the datasets from kaggle and International  Symposium on Biomedical Imaging (ISBI) 2019 contest  collection with 6,594 RGB photos. The data set contains  nine clinical types skin cancer, such as actinic keratosis,  basal cell carcinoma, benign keratosis, dermatofibroma,  melanoma, nevus, seborrheic keratosis, squamous cell  carcinoma, vascular lesions. The performance of the  proposed research has been correlated and compared with  the existing techniques i.e. Visual Geometry Group 16  (VGG16) and VGG19.
Keywords: Skin Cancer; CNN; Deep Learning; Kaggle; Python