Software-Engineering Perspectives on Machine for Skin-Disease Classification

Moomna Nazir; Azka Ahsan; Rabia Khadim; Shakeel Abbas; Aown Muhammad; Zain Sohail

doi:10.62762/JSE.2025.913699

Volume 2, Issue 1, ICCK Journal of Software Engineering

Volume 2, Issue 1, 2026

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ICCK Journal of Software Engineering, Volume 2, Issue 1, 2026: 52-70

Open Access | Review Article | 11 February 2026

Software-Engineering Perspectives on Machine for Skin-Disease Classification

Moomna Nazir 1 *

Azka Ahsan 2

Rabia Khadim 2

Shakeel Abbas 3

Aown Muhammad 4

Zain Sohail 2

1 Department of Computer Science, Govt. Post Graduate College for Women, Sahiwal 57040, Pakistan

2 Department of Computer Science, COMSATS University Islamabad, Sahiwal Campus, Sahiwal 57040, Pakistan

3 Department of Computer Science, Minhaj University Lahore, Lahore 54770, Pakistan

4 Department of Computer Science, University of Engineering and Technology, Lahore 54770, Pakistan

* Corresponding Author: Moomna Nazir, [email protected]

DOI: 10.62762/JSE.2025.913699

ARK: ark:/57805/jse.2025.913699

Received: 20 October 2025, Accepted: 16 December 2025, Published: 11 February 2026

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Abstract

Skin‑disease classification has evolved from simple image recognizers into software‑driven pipelines that demand reliability, reproducibility, and ethical governance. While most AI reviews focus on algorithmic accuracy, few examine these systems through a software‑engineering (SE) lens—essential for assessing pipeline modularity, version control, deployment readiness, and long‑term maintainability, all critical for clinical integration. This review surveys literature from 2015 to early 2025, curating about 180 papers that link skin‑disease classification with SE practices. It traces the shift from handcrafted feature‑based classifiers to end‑to‑end convolutional, ensemble, and transformer architectures, alongside the engineering processes that support versioning, deployment, and monitoring. Benchmark datasets (PH$^2$, HAM10000, ISIC, etc.) have established reproducible evaluation protocols that underpin software verification. Emerging directions—self‑supervised pretraining, multimodal fusion, human‑AI collaboration—signal a move from model‑centric to system‑level integration. The analysis highlights not only accuracy and generalization but also SE quality attributes: scalability, maintainability, explainability, and fairness, which are indispensable for trustworthy adoption in diverse clinical workflows.

Graphical Abstract

Keywords

software engineering

machine learning

deep learning

dermatology

computer-aided diagnosis

MLOps

fairness

Data Availability Statement

Data will be made available on request.

Funding

This work was supported without any funding.

Conflicts of Interest

The authors declare no conflicts of interest.

AI Use Statement

The authors declare that no generative AI was used in the preparation of this manuscript.

Ethical Approval and Consent to Participate

Not applicable. This is a review article based on publicly available literature and datasets; no human participants, primary data collection, or clinical interventions were involved.

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Nazir, M., Ahsan, A., Khadim, R., Abbas, S., Muhammad, A., & Sohail, Z. (2026). Software-Engineering Perspectives on Machine for Skin-Disease Classification. ICCK Journal of Software Engineering, 2(1), 52–70. https://doi.org/10.62762/JSE.2025.913699

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TY  - JOUR
AU  - Nazir, Moomna
AU  - Ahsan, Azka
AU  - Khadim, Rabia
AU  - Abbas, Shakeel
AU  - Muhammad, Aown
AU  - Sohail, Zain
PY  - 2026
DA  - 2026/02/11
TI  - Software-Engineering Perspectives on Machine for Skin-Disease Classification
JO  - ICCK Journal of Software Engineering
T2  - ICCK Journal of Software Engineering
JF  - ICCK Journal of Software Engineering
VL  - 2
IS  - 1
SP  - 52
EP  - 70
DO  - 10.62762/JSE.2025.913699
UR  - https://www.icck.org/article/abs/JSE.2025.913699
KW  - software engineering
KW  - machine learning
KW  - deep learning
KW  - dermatology
KW  - computer-aided diagnosis
KW  - MLOps
KW  - fairness
AB  - Skin‑disease classification has evolved from simple image recognizers into software‑driven pipelines that demand reliability, reproducibility, and ethical governance. While most AI reviews focus on algorithmic accuracy, few examine these systems through a software‑engineering (SE) lens—essential for assessing pipeline modularity, version control, deployment readiness, and long‑term maintainability, all critical for clinical integration. This review surveys literature from 2015 to early 2025, curating about 180 papers that link skin‑disease classification with SE practices. It traces the shift from handcrafted feature‑based classifiers to end‑to‑end convolutional, ensemble, and transformer architectures, alongside the engineering processes that support versioning, deployment, and monitoring. Benchmark datasets (PH$^2$, HAM10000, ISIC, etc.) have established reproducible evaluation protocols that underpin software verification. Emerging directions—self‑supervised pretraining, multimodal fusion, human‑AI collaboration—signal a move from model‑centric to system‑level integration. The analysis highlights not only accuracy and generalization but also SE quality attributes: scalability, maintainability, explainability, and fairness, which are indispensable for trustworthy adoption in diverse clinical workflows.
SN  - 3069-1834
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -

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@article{Nazir2026SoftwareEn,
  author = {Moomna Nazir and Azka Ahsan and Rabia Khadim and Shakeel Abbas and Aown Muhammad and Zain Sohail},
  title = {Software-Engineering Perspectives on Machine for Skin-Disease Classification},
  journal = {ICCK Journal of Software Engineering},
  year = {2026},
  volume = {2},
  number = {1},
  pages = {52-70},
  doi = {10.62762/JSE.2025.913699},
  url = {https://www.icck.org/article/abs/JSE.2025.913699},
  abstract = {Skin‑disease classification has evolved from simple image recognizers into software‑driven pipelines that demand reliability, reproducibility, and ethical governance. While most AI reviews focus on algorithmic accuracy, few examine these systems through a software‑engineering (SE) lens—essential for assessing pipeline modularity, version control, deployment readiness, and long‑term maintainability, all critical for clinical integration. This review surveys literature from 2015 to early 2025, curating about 180 papers that link skin‑disease classification with SE practices. It traces the shift from handcrafted feature‑based classifiers to end‑to‑end convolutional, ensemble, and transformer architectures, alongside the engineering processes that support versioning, deployment, and monitoring. Benchmark datasets (PH\$^2\$, HAM10000, ISIC, etc.) have established reproducible evaluation protocols that underpin software verification. Emerging directions—self‑supervised pretraining, multimodal fusion, human‑AI collaboration—signal a move from model‑centric to system‑level integration. The analysis highlights not only accuracy and generalization but also SE quality attributes: scalability, maintainability, explainability, and fairness, which are indispensable for trustworthy adoption in diverse clinical workflows.},
  keywords = {software engineering, machine learning, deep learning, dermatology, computer-aided diagnosis, MLOps, fairness},
  issn = {3069-1834},
  publisher = {Institute of Central Computation and Knowledge}
}

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