Back close

DTP-Net: A convolutional neural network model to predict threshold for localizing the lesions on dermatological macro-images

Publication Type : Journal Article

Publisher : Elsevier

Source : Computers in Biology and Medicine: Elsevier (IF - 7.7, indexed in SCI), vol. 148, September 2022.

Url :

Campus : Coimbatore

School : School of Artificial Intelligence

Center : Computational Engineering and Networking

Year : 2022

Abstract : 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 ‘Deep Threshold Prediction Network (DTP-Net)’, 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 Otsu’s thresholding, Valley emphasized otsu’s thresholding, Isodata thresholding, Histogram slope difference distribution-based thresholding, Minimum error thresholding, Poisson’s distribution-based minimum error thresholding, Kapur’s maximum entropy thresholding, Entropy-weighted otsu’s 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.

Cite this Research Publication : Vipin Venugopal, Justin Joshep, M. Vipin Das, Malaya Kumar Nath, "DTP-Net: A Convolutional Neural Network Model to Predict Threshold for Localizing the Lesions on Dermatological Macro-images", Computers in Biology and Medicine: Elsevier (IF - 7.7, indexed in SCI), vol. 148, September 2022.

Admissions Apply Now