AUTHOR=Ghosh Pronab , Azam Sami , Quadir Ryana , Karim Asif , Shamrat F. M. Javed Mehedi , Bhowmik Shohag Kumar , Jonkman Mirjam , Hasib Khan Md. , Ahmed Kawsar 

TITLE=SkinNet-16: A deep learning approach to identify benign and malignant skin lesions

JOURNAL=Frontiers in Oncology

VOLUME=Volume 12 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.931141

DOI=10.3389/fonc.2022.931141

ISSN=2234-943X

ABSTRACT=Skin cancer in modern times has become one of the most ubiquitous types of cancer. Accurate identification of cancerous skin lesions is of vital importance in treating this malady. In this research, we employed a deep learning approach to identify benign and malignant skin lesions. The initial dataset was obtained from Kaggle before several preprocessing steps for hair and background removal, image enhancement, selection of the Region of Interest (ROI), Region Based Segmentation, Morphological Gradient and feature extraction were performed, resulting in histopathological images data with 20 input features based on geometrical and textural features. A Principle Component Analysis (PCA) based feature extraction technique was put into action to reduce the dimensionality to 10 input features. Subsequently, we applied our deep learning classifier, SkinNet – 16, to detect the cancerous lesion accurately at a very early stage. The highest accuracy was obtained with the Adamax optimizer with a learning rate of 0.006 from the neural network based model developed in this study. The model also delivered an impressive accuracy of approximately 99.19%.