Classification of Skin Lesions in Dermatoscopic Images with Deep Convolution Network

Abstract

In this study, a deep learning based solution with convolutional neural network is presented to solve the problem of classification of dermoscopic images containing skin lesions. Defining models with classical machine learning techniques takes a lot of time and with this model, it cannot make data meaningful without pretreatment. Thanks to deep learning, we have come a long way in problems that we think are difficult to solve for many years. Deep learning achieves results by processing the data at hand without any intervention by us. Classification of dermoscopic images of skin lesions is a difficult task to distinguish between benign or malignant melanocytic tumors. Malignant melanoma is the deadliest form of skin cancer and is one of the fastest developing cancers in the world. If diagnosed early, it can be easily treated and ultimately, early diagnosis of melanoma is vital. Dermoscopy has become one of the most important tools in the diagnosis of melanoma and other pigmented skin lesions. Due to the inaccuracy, subjectivity, and poor reproducibility of human judgment, there has been a need to process the dermoscopy image with an automatic recognition algorithm. In 2017, the support vector machine (SVM) classifier was used to differentiate 172 dermatoscopic images into two classes as “benign”and “malignant”. Experiments on the dataset have 91% accuracy. However, the fact that we have thousands of images in our data set and that we will break them down into seven lesion classes required us to search for more effective methods. Classroom inconsistency of melanomas is considered a challenging process due to the low contrast of skin lesions and artificial objects such as noise, presence of hair, air bubbles and similarity between non-melanoma cases in dermoscopy images. To solve these problems, we propose the VGGNET-16 architecture, which includes a powerful convolutional neural network model to classify seven different types of disease on dermoscopic images. “HAM10000 ”(Human Againist Machine) data set was used with VGGNET-16 architecture and the results were observed. The data set, which is published as an educational set for academic machine learning and made public through the ISIC archive, consists of 10015 dermatoscopic images. K-Fold Cross Validation technique was used to differentiate the data set consisting of seven lesion classes as training and test area. In the test phase of the educated model, the validation of the classes was obtained as 85.62%.