Abstract
Biomedical image analysis has been exploited considerably by recent technology involvements, carrying about a pattern shift towards ‘automation’ and ‘error free diagnosis’ classification methods with markedly improved accurate diagnosis productivity and cost effectiveness. This paper proposes an automated deep learning model to diagnose skin disease at an early stage by using Dermoscopy images. The proposed model has four convolutional layers, two maxpool layers, one fully connected layer and three dense layers. All the convolutional layers are using the kernel size of 3 * 3 whereas the maxpool layer is using the kernel size of 2 * 2. The dermoscopy images are taken from the HAM10000 dataset. The proposed model is compared with the three different models of ResNet that are ResNet18, ResNet50 and ResNet101. The models are simulated with 32 batch size and Adadelta optimizer. The proposed model has obtained the best accuracy value of 0.96 whereas the ResNet101 model has obtained 0.90, the ResNet50 has obtained 0.89 and the ResNet18 model has obtained value as 0.86. Therefore, features obtained from the proposed model are more capable for improving the classification performance of multiple skin disease classes. This model can be used for early diagnosis of skin disease and can also act as a second opinion tool for dermatologists.
Highlights
Skin is an outer covering that separates the environment and body of human beings
The features obtained from the proposed model are capable of improving the performance of classification for multiple skin disease classes
Whereas dotted curve lines double the number of channels and halve the feature map dimension by using stride of 2
Summary
Skin is an outer covering that separates the environment and body of human beings. It is the largest organ of the body. Codella et al [20] combined latest ML techniques with deep residual networks and CNN for segmentation of dermoscopy images in order to resolve the skin disease classification issue. They worked on the dataset consisting of 900 dermoscopic images. From the ISIC archive and HAM10000 dataset 12,927 images were extracted It had achieved an accuracy of 86.2%. Dorj et al [24] mainly focused on classification of skin cancer using SVM and CNN They had used a pre-trained AlexNet model for extracting features. A new Convolutional Neural Network (CNN) based model has been proposed for the classification of skin disease using dermoscopy images The major contributions of the study are as follows: 1. A new Convolutional Neural Network (CNN) based model has been proposed for the classification of skin disease using dermoscopy images
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