DTP-Net: A convolutional neural network model to predict threshold for localizing the lesions on dermatological macro-images
Highly focused images of skin captured with ordinary cameras, called macro-images, are extensively used in dermatology. Being highly focused views, the macro-images contain only lesions and background regions. Hence, the localization of lesions on the macro-images is a simple thresholding problem. However, algorithms that offer an accurate estimate of threshold and retain consistent performance on different dermatological macro-images are rare. A deep learning model, termed âDeep Threshold Prediction Network (DTP-Net)â, is proposed in this paper to address this issue. For training the model, grayscale versions of the macro-images are fed as input to the model, and the corresponding gray-level threshold values at which the Dice similarity index (DSI) between the segmented and the ground-truth images are maximized are defined as the targets. The DTP-Net exhibited the least value of root mean square error for the predicted threshold compared with 11 state-of-the-art threshold estimation algorithms (such as Otsuâs thresholding, Valley emphasized otsuâs thresholding, Isodata thresholding, Histogram slope difference distribution-based thresholding, Minimum error thresholding, Poissonâs distribution-based minimum error thresholding, Kapurâs maximum entropy thresholding, Entropy-weighted otsuâs thresholding, Minimum cross-entropy thresholding, Type-2 fuzzy-based thresholding, and Fuzzy entropy thresholding). The DTP-Net could learn the difference between the lesion and background in the intensity space and accurately predict the threshold that separates the lesion from the background. The proposed DTP-Net can be integrated into the segmentation module in automated tools that detect skin cancer from dermatological macro-images.
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University of Waterloo skin cancer database