Psoriasis skin biopsy image segmentation using Deep Convolutional Neural Network
Abstract
Background and objective Development of machine assisted tools for automatic analysis of psoriasis skin biopsy image plays an important role in clinical assistance. Development of automatic approach for accurate segmentation of psoriasis skin biopsy image is the initial prerequisite for developing such system. However, the complex cellular structure, presence of imaging artifacts, uneven staining variation make the task challenging. This paper presents a pioneering attempt for automatic segmentation of psoriasis skin biopsy images.Methods Several deep neural architectures are tried for segmenting psoriasis skin biopsy images. Deep models are used for classifying the super-pixels generated by Simple Linear Iterative Clustering (SLIC) and the segmentation performance of these architectures is compared with the traditional hand-crafted feature based classifiers built on popularly used classifiers like K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Random Forest (RF). A U-shaped Fully Convolutional Neural Network (FCN) is also used in an end to end learning fashion where input is the original color image and the output is the segmentation class map for the skin layers.Results An annotated real psoriasis skin biopsy image data set of ninety (90) images is developed and used for this research. The segmentation performance is evaluated with two metrics namely, Jaccard's Coefficient (JC) and the Ratio of Correct Pixel Classification (RCPC) accuracy. The experimental results show that the CNN based approaches outperform the traditional hand-crafted feature based classification approaches.Conclusions The present research shows that practical system can be developed for machine assisted analysis of psoriasis disease.
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The field of inflammatory dermatopathology is largely unexplored by AI systems.
“On the other hand, the field of inflammatory dermatopathology is largely unexplored by AI systems [11,16,17].”
From Artificial Intelligence in the Histopathological Assessment of Non-Neoplastic Skin Disorders: A Narrative Review with Future Perspectives (2025) · cites this paper as [17]
Pal et al. developed a U-shaped CNN using 90 psoriasis biopsy pictures that successfully analyzed super pixels and classified skin layers, outperforming other hand-crafted feature-based classifying models.
“This was achieved by Pal et al. [17], who developed a U-shaped CNN using a dataset of 90 psoriasis biopsy pictures: the CNN model successfully analyzed super pixels and classified the skin layers, outperforming other hand-crafted feature-based classifying models.”
From Artificial Intelligence in the Histopathological Assessment of Non-Neoplastic Skin Disorders: A Narrative Review with Future Perspectives (2025) · cites this paper as [17]
Pal et al.'s U-shaped deep CNN segmented psoriasis skin biopsy images, effectively distinguishing the epidermis and dermis despite challenges like uneven staining and complex cellularity.
“A study by Pal et al. developed a U-shaped deep CNN to segment psoriasis skin biopsy images, effectively distinguishing the epidermis and dermis despite challenges like uneven staining and complex cellularity [29].”
From Artificial Intelligence in Dermatopathology: An Update and Review of the Current Literature (2026) · cites this paper as [29]
Pal et al.'s model outperformed traditional feature-based methods, demonstrating the potential of deep learning for analyzing inflammatory skin diseases.
“The model outperformed traditional feature-based methods, demonstrating the potential of deep learning for analyzing inflammatory skin diseases.”
From Artificial Intelligence in Dermatopathology: An Update and Review of the Current Literature (2026) · cites this paper as [29]
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- Authors
- Pal, A., Garain, U., Chandra, A., Chatterjee, R., & Senapati, S.
- Affiliations
- Indian Statistical Institute (IN)
- Corresponding author
- Pal, Anabik
- Venue
- Computer Methods and Programs in Biomedicine
- Publisher
- Elsevier BV
- Published
- 2018-06
- Volume
- 159
- Pages
- 59-69
- ISSN
- 0169-2607 (ISSN-L)
- OpenAlex
- W2794251468
- Cited by
- 74
- References
- 52
- Citations by year
- 2026: 3 · 2025: 11 · 2024: 7 · 2023: 9 · 2022: 10 · 2021: 11 · 2020: 14 · 2019: 8 · 2018: 1
- FWCI
- 4.25
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Computer Methods and Programs in Biomedicine (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- psoriasis, image segmentation, deep convolutional neural networks, superpixel classification, SLIC, fully convolutional network
- MeSH terms
- Algorithms, Biopsy, Color, Epidermis, Humans, Image Processing, Computer-Assisted, Psoriasis, Skin, Reproducibility of Results, Models, Statistical, Cluster Analysis, Databases, Factual, Neural Networks, Computer, Dermis, Support Vector Machine
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