Skin Lesions Detection using Deep Learning Techniques

Abstract

The main category of cancer is skin cancer, which is manifested by certain skin diseases. They have been constrained into various typologies stationed on morphological features, color, structure, and texture. Due to certain factors, the study equips analysis efforts to advance algorithms with greater closeness and tensility in detecting early-stage melanoma. As per the ACS report, melanoma is one of the recurrent cancers in the world. In 2017, around 87,110 new cases were diagnosed in the United States alone. Dermoscope images contain imperfections such as shadows, artifacts, and noise that degrade image essence. To overthrow these objection, deep learning neural networks have been used to detect skin cancer by tampering images. In the proposed work, automatic classification of skin lesions is proposed. Image classification, object detection etc are some of the computer vision tasks in deep CNN which have driven DCNNs to be reliable on several substitute assumptions, tiling its way for new exploration areas. CNN attained performance equivalent to all experts tested, achieving an exact competence equivalent to dermatologists treating skin cancer. In this article, we attempt to improve the Deep Convolutional Neural Networks example using the ImageNet dataset with HAM10000, fortuitously classifying seven categories of skin lesions. HAM10000 is a dataset of 10000 dermoscope images. Layers are fine-tuned applying separate approach such as InceptionV3, InceptionResNet, DenseNet and VGG-16. Over previous years, the power of deep learning-based approaches has enhanced fiercely and their work come out to outperform common image processing approach on classification tasks. However, these categories of machine learning-based accession have important drawbacks. Training requires thousands of annotated images for each class. The idea is to use deep learning algorithms and available dataset assets to bear models with higher accuracy and best results.