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Computer Sciences & Mathematics Forum
Volume 12
Issue 1
10.3390/cmsf2025012011
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Proceeding Paper

A Multiscale Convolutional Neural Network Framework for Automated Segmentation and Pattern Mapping of Psoriatic Lesions †

by
Anagha Kulkarni
1,*,
Priyanka Pawar
1,
Bhavana Pansare
1 and
Harshal Raje
2
1
Dr. D. Y. Patil School of Science and Technology, Dr. D. Y. Patil Vidyapeeth, Pimpri, Pune 411033, India
2
Global Business School and Research Centre, Dr. D. Y. Patil Vidyapeeth, Pimpri, Pune 411033, India
*
Author to whom correspondence should be addressed.
†
Presented at the First International Conference on Computational Intelligence and Soft Computing (CISCom 2025), Melaka, Malaysia, 26–27 November 2025.
Comput. Sci. Math. Forum 2025, 12(1), 11; https://doi.org/10.3390/cmsf2025012011
Published: 19 December 2025
(This article belongs to the Proceedings of First International Conference on Computational Intelligence and Soft Computing (CISCom 2025))
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Abstract

For psoriatic lesions, automatic segmentation is crucial to perform objective assessment, monitoring, and medication planning in dermatology. This study proposes a Multiscale Convolutional Neural Network (MSCNN) framework for precise segmentation of psoriatic lesions from medical images. The model was evaluated using the ISIC 2017 dataset and demonstrated robust performance in lesion localization. By analyzing the predicted masks, it has been observed that the boundaries closely match ground truth annotations. Metrics for quantitative evaluation are Dice Coefficient, Intersection over Union (IoU), used with high precision and slightly lower recall, reflecting occasional under-segmentation of fine-scale lesion details. The findings focus on the proposed MSCNN’s capability to produce reliable lesion masks, while also detecting areas for advancement in capturing irregular lesion boundaries. Future work involves integrating multimodal imaging, attention mechanisms, and larger, diverse datasets to improve segmentation accuracy and clinical applicability.
Keywords: psoriasis; Multiscale Convolutional Neural Network (MSCNN); deep learning; medical image analysis; dice coefficient; Intersection over Union (IoU) psoriasis; Multiscale Convolutional Neural Network (MSCNN); deep learning; medical image analysis; dice coefficient; Intersection over Union (IoU)

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MDPI and ACS Style

Kulkarni, A.; Pawar, P.; Pansare, B.; Raje, H. A Multiscale Convolutional Neural Network Framework for Automated Segmentation and Pattern Mapping of Psoriatic Lesions. Comput. Sci. Math. Forum 2025, 12, 11. https://doi.org/10.3390/cmsf2025012011

AMA Style

Kulkarni A, Pawar P, Pansare B, Raje H. A Multiscale Convolutional Neural Network Framework for Automated Segmentation and Pattern Mapping of Psoriatic Lesions. Computer Sciences & Mathematics Forum. 2025; 12(1):11. https://doi.org/10.3390/cmsf2025012011

Chicago/Turabian Style

Kulkarni, Anagha, Priyanka Pawar, Bhavana Pansare, and Harshal Raje. 2025. "A Multiscale Convolutional Neural Network Framework for Automated Segmentation and Pattern Mapping of Psoriatic Lesions" Computer Sciences & Mathematics Forum 12, no. 1: 11. https://doi.org/10.3390/cmsf2025012011

APA Style

Kulkarni, A., Pawar, P., Pansare, B., & Raje, H. (2025). A Multiscale Convolutional Neural Network Framework for Automated Segmentation and Pattern Mapping of Psoriatic Lesions. Computer Sciences & Mathematics Forum, 12(1), 11. https://doi.org/10.3390/cmsf2025012011

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