| Summary: | Skin cancer becomes a significant health problem worldwide with an increasing incidence over the past decades. Due to the fine-grained differences in the appearance of skin lesions, it is very challenging to develop an automated system for benign-malignant classification through images. This paper proposes a novel automated Computer Aided Diagnosis (CAD) system for skin lesion classification with high classification performance using accuracy low computational complexity. A pre-processing step based on morphological filtering is employed for hair removal and artifacts removal. Skin lesions are segmented automatically using Grab-cut with minimal human interaction in HSV color space. Image processing techniques are investigated for an automatic implementation of the ABCD (asymmetry, border irregularity, color and dermoscopic patterns) rule to separate malignant melanoma from benign lesions. To classify skin lesions into benign or malignant, different pretrained convolutional neural networks (CNNs), including VGG-16, ResNet50, ResNetX, InceptionV3, and MobileNet are examined. The average 5-fold cross validation results show that ResNet50 architecture combined with Support Vector Machine (SVM) achieve the best performance. The results also show the effectiveness of data augmentation in both training and testing with achieving better performance than obtaining new images. The proposed diagnosis framework is applied to real clinical skin lesions, and the experimental results reveal the superior performance of the proposed framework over other recent techniques in terms of area under the ROC curve 99.52%, accuracy 99.87%, sensitivity 98.87%, precision 98.77%, F1-score 97.83%, and consumed time 3.2 s. This reveals that the proposed framework can be utilized to help medical practitioners in classifying different skin lesions. © 2022, The Author(s).
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