Skin Lesion Classification Based on Convolutional Neural Networks (original) (raw)
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Skin cancer, also known as melanoma, is generally diagnosed visually from the dermoscopic images, which is a tedious and time-consuming task for the dermatologist. Such a visual assessment, via the naked eye for skin cancers, is a challenging and arduous due to different artifacts such as low contrast, various noise, presence of hair, fiber, and air bubbles, etc. This article proposes a robust and automatic framework for the Skin Lesion Classification (SLC), where we have integrated image augmentation, Deep Convolutional Neural Network (DCNN), and transfer learning. The proposed framework was trained and tested on publicly available IEEE International Symposium on Biomedical Imaging (ISBI)-2017 dataset. The obtained average area under the receiver operating characteristic curve (AUC), recall, precision, and F1-score are respectively 0.87, 0.73, 0.76, and 0.74 for the SLC. Our experimental studies for lesion classification demonstrate that the proposed approach can successfully disti...
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This paper focuses on the classification of dermoscopic images to identify the type of Skin lesion whether it is benign or malignant. Dermoscopic images provide deep insight for the analysis of any type of skin lesion. Initially, a custom Convolutional Neural Network (CNN) model is developed to classify the images for lesion identification. This model is trained across different train-test split and 30% split of train data is found to produce better accuracy. To further improve the classification accuracy a Batch Normalized Convolutional Neural Network (BN-CNN) is proposed. The proposed solution consists of 6 layers of convolutional blocks with batch normalization followed by a fully connected layer that performs binary classification. The custom CNN model is similar to the proposed model with the absence of Batch normalization and presence of Dropout at Fully connected layer. Experimental results for the proposed model provided better accuracy of 89.30%. Final work includes analysis of the proposed model to identify the best tuning parameters.