Deep Learning–Based Methods for Automatic Diagnosis of Skin Lesions

Abstract

The main purpose of the study was to develop a high accuracy system able to diagnose skin lesions using deep learning–based methods. We propose a new decision system based on multiple classifiers like neural networks and feature–based methods. Each classifier (method) gives the final decision system a certain weight, depending on the calculated accuracy, helping the system make a better decision. First, we created a neural network (NN) that can differentiate melanoma from benign nevus. The NN architecture is analyzed by evaluating it during the training process. Some biostatistic parameters, such as accuracy, specificity, sensitivity, and Dice coefficient are calculated. Then, we developed three other methods based on convolutional neural networks (CNNs). The CNNs were pre-trained using large ImageNet and Places365 databases. GoogleNet, ResNet-101, and NasNet-Large, were used in the enumeration order. CNN architectures were fine-tuned in order to distinguish the different types of skin lesions using transfer learning. The accuracies of the classifications were determined. The last proposed method uses the classical method of image object detection, more precisely, the one in which some features are extracted from the images, followed by the classification step. In this case, the classification was done by using a support vector machine. Just as in the first method, the sensitivity, specificity, Dice similarity coefficient and accuracy are determined. A comparison of the obtained results from all the methods is then done. As mentioned above, the novelty of this paper is the integration of these methods in a global fusion-based decision system that uses the results obtained by each individual method to establish the fusion weights. The results obtained by carrying out the experiments on two different free databases shows that the proposed system offers higher accuracy results.

Malignant Melanoma Classification Using Deep Learning: Datasets, Performance Measurements, Challenges and Opportunities

“…Attique et al [59] incorporated optimized color feature segmentation with CNN, and it uses ISBI 2016, ISBI 2017 and ISBI 2018 datasets to check the effectiveness of its proposed method. El-Khatiab et al [81] use transfer learning which was based on Google Net, ResNet and NasNet. To validate the proposed method PH2 and ISBI 2019 were used.…”

FCN-Based DenseNet Framework for Automated Detection and Classification of Skin Lesions in Dermoscopy Images

“…The performance evaluation of these models on medical images shows that they are still yet to outperform the state-ofthe-art. For example, El-Khatib et al [51] applied the transfer learning approach on CNN models which were already pretrained on ImageNet and Places365 datasets. They also used other pre-trained models such as GoogleNet, ResNet-101, and NasNet-Large.…”

Melanoma Detection Using an Objective System Based on Multiple Connected Neural Networks

“…In our previous work [31] we used a global fusion-based decision system consisting of the following classifiers: twolayer-feed-forward network, GoogleNet CNN, ResNet-101 CNN, NasNet-Large CNN (all deep CNN with transfer learning), and HOG based SVM. The decision fusion is based on a threshold of 0.7 from the total weights.…”

“…The individual classifier decisions were considered 0 for nonmelanoma (NMe) and 1 for melanoma (Me). Unlike [31], we propose a system based on two classification stages: the first stage uses different aspects of melanoma: texture, shape and color characteristics, and particular features extracted inside deep convolutional networks (end-to-end). These are seen as subjective classifiers.…”