A Deep Neural Network Approach to Detect and Classify Skin Cancer
DOI:
https://doi.org/10.55524/Keywords:
Skin Cancer, CNN, Deep Learning, Kaggle, PythonAbstract
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.
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