{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/dtp-net-a-convolutional-neural-network-model","title":"DTP-Net: A convolutional neural network model to predict threshold for localizing the lesions on dermatological macro-images","arxiv_id":null,"date":"2022-07-12","proceeding":"Computers in Biology and Medicine 2022 7","authors":["Vipin Venugopal","Justin Joseph","M Vipin Das","Malaya Kumar Nath"],"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.","url_abs":"https://www.sciencedirect.com/science/article/pii/S0010482522006047","url_pdf":"https://doi.org/10.1016/j.compbiomed.2022.105852","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"dtp-net-a-convolutional-neural-network-model","repo_url":"https://github.com/VipinBioLab/DTP-Net","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"lesion-segmentation","task_name":"Lesion Segmentation"},{"task_slug":"skin-lesion-segmentation","task_name":"Skin Lesion Segmentation"}],"methods":[{"method_slug":"dcnn","method_name":"DCNN"}],"datasets_introduced":[{"slug":"university-of-waterloo-skin-cancer-database","name":"University of Waterloo skin cancer database","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/lesion-segmentation-on-university-of-waterloo","task":"Lesion Segmentation","dataset":"University of Waterloo skin cancer database","model":"DTP-Net","rank_in_archive_order":1,"of":5,"metrics":{"Dice score":"0.884 ±0.100"},"uses_additional_data":false},{"leaderboard":"/sota/skin-lesion-segmentation-on-university-of","task":"Skin Lesion Segmentation","dataset":"University of Waterloo skin cancer database","model":"DTP-Net","rank_in_archive_order":1,"of":5,"metrics":{"Dice Score":"0.884 ±0.100"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}