# Comparison of Deep Transfer Learning Techniques in Human Skin Burns Discrimination
## 2 Literature Review  
Deep learning evolved from a tiny architecture called Artificial Neural Network (ANN), popularly known for classification task which was inspired by human brain. Perceptron is a basic unit of ANN which has an associated weight, bias and then a manipulative function applied to supplied input to obtain a desired outcome. During the process, both weights and biases are adjusted regularly until a convincing or satisfactory output is generated by the algorithm. This process of adjusting weights and biases is called Back-propagation. The introduction of non-linearity function made neural network more successful and deeper enough to extract complex patterns from the data. The deeper version of ANN is called deep neural network with many variations such as Convolutional Neural Network (CNN). In CNN, convolution layers are equipped with filters or kernels that slide over a given image to extract useful patterns and the output of the sliding operation is known as feature map which is then made available as input to next layer. However, the success of deep learning algorithms lies with the availability of enough training data and powerful computational machines. CNN have achieved state-of-the-art recognition accuracies in many classification problems including plant disease detection [13], cancer [14; 15] and skin burns assessment [9]. A study in Reference [9] to discriminate burns and healthy skin using off-the-shelf features was proposed. The study utilized a deep pre-trained neural network model to extract patterns from the given images. This was done by passing the images through the base convolution layers of the adopted model without the top classification layer, following pre-processing steps such normalization and resizing processes. Support Vector Machine (SVM) was used as replacement of the classification layer and was trained using feature scores obtained from the convolution operations. 10-folds cross-validation strategy was applied during SVM training and interestingly, 99.5% accuracy was achieved in assessing whether a given image is burnt or healthy. Skin burns are categorized into classes depending on whether the injury affects the top-most layer, intermediate or deep layer. In this regard, the authors of Reference [16] proposed a study to classify healthy skin, superficial and deep dermal (full-thickness) burns using pre-trained deep learning model and SVM. Specifically, they used ResNet101 for the extraction of image feature extraction and SVM for the classification task, in which identification accuracy of 99.9% was achieved using 10-folds cross-validation strategy. In similar development, research by the authors of Reference [10] use of deep learning models to assess whether a given image is superficial burn, superficial to intermediate partial thickness burns, intermediate to deep partial thickness burns, deep partial and full thickness burns, healthy skin or background image. Four pre-trained deep learning models were used to classify the burn images transfer learning approach, in which earlier convolution layers were frozen to extract features and the top-most layers were modified and trained using features obtained from the frozen layers. This process was applied to several deep neural network models including VGG-16 which recorded 86.7% accuracy, 79.4% for GoogleNet, 82.4% for ResNet-50 and 88.1% for ResNet-101. Studies in Reference [17] proposed the use of off-the-shelf features with SVM classifier to classify burns in both Caucasian and African patients. Off-the-shelf features were extracted using three pre-trained deep learning models which include VGG-16, VGG-19 and VGG-Face while in each case SVM was used as the classifier. Classification accuracy on Caucasian images, VGG-16, VGG-19 and VGG-Face achieved 99.3% 98.3% and 96.3% respectively. Using African images, VGG-16, VGG-19 and VGG-Face achieved 98.9%, 97.5% and 97.2% respectively.