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Paper ID:IJSREM65068

Skin Disease Diagnosis System Using Machine Learning

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM65068

Volume:

10 |

6 |

Jun 2026

Pages:

1-4

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM65068

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

MB

Maheshwari Badgire

Department of Computer Engineering

Sinhgad Academy of Engineering,Kondhwa BK,Pune

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DM

Dhanashree Mane

Department of Computer Engineering

Sinhgad Academy of Engineering,Kondhwa BK,Pune

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NH

Nandini Halse

Department of Computer Engineering

Sinhgad Academy of Engineering,Kondhwa BK,Pune

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MK

Mayuri Kuber

Department of Computer Engineering

Sinhgad Academy of Engineering,Kondhwa BK,Pune

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Mentors

MN

Mahendra Nivangune

Department of Computer Engineering

Sinhgad Academy of Engineering,Kondhwa BK,Pune

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Abstract

Skin diseases are among the most prevalent health problems worldwide, affecting millions of people across different age groups and regions. Early and accurate diagnosis is essential to prevent severe complications, particularly in life-threatening cases such as melanoma. However, manual diagnosis through clinical examination or biopsy is often time-consuming, costly, and subjective, leading to inconsistent outcomes. To overcome these limitations, this study proposes an automated Skin Disease Diagnosis System using Machine Learning, which leverages Deep Learning (DL) and Transfer Learning (TL) techniques for precise classification of dermatological images. The proposed system employs a Convolutional Neural Network (CNN) architecture using pre-trained MobileNetV2 and EfficientNetB0 models, fine-tuned on a curated dataset of multiple skin disease classes including benign, malignant (melanoma), eczema, psoriasis, fungal, and normal skin. The images are preprocessed through resizing, normalization, and augmentation to enhance generalization and minimize overfitting.

KEYWORDS

Skin Disease Detection Deep Learning Machine Learning MobileNetV2 EfficientNetB0 Transfer Learning CNN Image Classification.

Cite This Article

Maheshwari Badgire, Dhanashree Mane, Nandini Halse, Mayuri Kuber, Mahendra Nivangune, "Skin Disease Diagnosis System Using Machine Learning," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 6, pp. 1-4, Jun 2026. https://doi.org/10.55041/IJSREM65068

Full Article/Conclusion

The Skin Diseases Diagnosis System using Machine Learning successfully demonstrates how artificial intelligence can assist in the early detection and classification of skin diseases. By utilizing advanced image processing and machine learning algorithms, the system provides accurate, fast, and automated diagnostic support to users and healthcare professionals.This system bridges the gap between patients and dermatologists by offering a digital solution that can be accessed remotely, making healthcare more inclusive and efficient. The results prove that machine learning models can achieve high accuracy when trained on quality datasets, thereby reducing diagnostic time and improving patient outcomes.

References

[1] Keshetti Sreekala, N. Rajkumar, R. Sugumar, K. V. Daya Sagar, R. Shobarani, K. Parthiban Krishnamoorthy, A. K. Saini, H. Palivela, A. Yeshitla, "Skin Diseases Classification Using Hybrid AI Based Localization Approach", Computational Intelligence and Neuroscience, vol. 2022, Article ID 6138490, 7 pages, 2022. https://doi.org/10.1155/2022/6138490. [2]  M. Malciu, M. Lupu, and V. M. Voiculescu, “Artificial intelligence-based approaches to reflectance confocal microscopy image analysis in Dermatology,” Journal of Clinical Medicine, vol. 11, no. 2, p. 429, 2022. [3]  M. A. Khan, M. Sharif, T. Akram, R. Damaševičius, and R. Maskeliūnas, “Skin lesion segmentation and multiclass classification using deep learning features and improved moth flame optimization,” Diagnostics, vol. 11, no. 5, p. 811, 2021. [4]  P. Thapar, M. Rakhra, G. Cazzato, and M. S. Hossain, “A novel hybrid deep learning approach for skin lesion segmentation and classification,” Journal of Healthcare Engineering, vol. 2022, Article ID 1709842, 21 pages, 2022. [5]  Fawaz Waselallah Alsaade, Theyazn H. H. Aldhyani, Mosleh Hmoud Al-Adhaileh, "Developing a Recognition System for Diagnosing Melanoma Skin Lesions Using Artificial Intelligence Algorithms", Computational and Mathematical Methods in Medicine, vol. 2021, Article ID 9998379, 20 pages, 2021. https://doi.org/10.1155/2021/9998379. [6]  Mehak Arshad, Muhammad Attique Khan, Usman Tariq, Ammar Armghan, Fayadh Alenezi, Muhammad Younus Javed, Shabnam Mohamed Aslam, Seifedine Kadry, "A Computer-Aided Diagnosis System Using Deep Learning for Multiclass Skin Lesion Classification", Computational Intelligence and Neuroscience, vol. 2021, Article ID 9619079,15 pages, 2021. https://doi.org/10.1155/2021/9619079. [7]  Li-sheng Wei, Quan Gan, Tao Ji, "Skin Disease Recognition Method Based on Image Color and Texture Features", Computational and Mathematical Methods in Medicine, vol. 2018, Article ID 8145713, 10 pages, 2018. https://doi.org/10.1155/2018/8145713.022. [8]  B. Sahu, A. Sontakke, K. Mahamuni, S. Akade, and R. Sutar,“Skin Disease Detection using Machine Learning,” International Journal of Advanced Research in Science, Communication and Technology (IJARSCT), vol. 3, no. 3, pp. 317–322, Dec. 2023. [9]  A. Tschandl, C. Rosendahl, and H. Kittler, “The HAM10000 dataset: A large collection of multi-source dermatoscopic images of common pigmented skin lesions,” Scientific Data, vol. 5, Article no. 180161, Aug. 2018. [10] G. Esteva, B. Kuprel, R. Novoa, J. Ko, S. Swetter, H. Blau, and S. Thrun, “Dermatologist-level classification of skin cancer with deep neural networks,” Nature, vol. 542, pp. 115–118, 2017. [11] R. Chatterjee, S. Dey, and A. Mukherjee, “Automated Detection of Skin Diseases Using Deep Convolutional Neural Networks,” Procedia Computer Science, vol. 167, pp. 254–263, 2020.

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Publication Date

6/17/2026

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Double-Blind Peer Review

Review Rounds: 2

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