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

Abstract

Skin cancer these days have become quite a common occurrence especially in certain geographic areas such as Oceania. Early detection of such cancer with high accuracy is of utmost importance, and studies have shown that deep learning- based intelligent approaches to address this concern have been fruitful. In this research, we present a novel deep learning- based classifier that has shown promise in classifying this type of cancer on a relevant preprocessed dataset having important features pre-identified through an effective feature extraction method.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%.

COVID-19 infection segmentation using hybrid deep learning and image processing techniques

“…Deep Learning (DL) techniques have shown promising potential in this area due to their ability to analyze enormous amounts of data rapidly and reliably 11 – 17 . Even prior to the COVID-19 epidemic, DL techniques demonstrated noteworthy performance in a variety of research applications, including object recognition 18 , 19 , face recognition 20 , object tracking 21 , steganography 22 , and medical image analysis 23 , 24 , as well as in disease detection, such as skin cancer detection, breast cancer detection, lung segmentation and detection of malaria on blood smear images 25 – 29 . In medical imaging analysis, issues with COVID-19 detection can be interpreted as classification and segmentation to identify and detect abnormal features and regions of interest (ROIs) using DL techniques.…”

Skin Cancer Detection Using Deep Learning—A Review

“…Ghosh et al [56] proposed an AI-based framework for the classification of skin cancers. A CNN-based SkinNet-16 classifier trained on ISIC and HAM10000 dataset was proposed to classify skin images.…”

“…Principal component analysis (PCA) [57] was used to reduce the dimensionality of the dataset. Ghosh et al [56] tested different optimizers and achieved the maximum validation accuracy of 95.51% on HAM10000 and 99.19% on ISIC dataset using Adamax optimizer. The proposed method was only tested on a binary classification task; the performance of the proposed method should be evaluated on multi-class classification problems and on large datasets.…”

SkinNet-INIO: Multiclass Skin Lesion Localization and Classification Using Fusion-Assisted Deep Neural Networks and Improved Nature-Inspired Optimization Algorithm

“…They fine-tuned the architecture by employing transfer learning (TL) to differentiate the various forms of skin lesions and achieved an accuracy level of 90% on a well-known PH2 database. Alizadeh et al [ 43 ] deployed the Vgg19 NN model using kernel principal components analysis (KPCA) and attained 85.2% accuracy using the ISIC2016 dataset. Almaraz et al [ 44 ] used the ABCD rule-based technique after extracting handcrafted features’ color, shapes, and texture.…”