Loading article...
Loading article...
Stream/Area:
-
Subject:
-
Paper ID:
IJSREM65068
Volume:
10 |
6 |
Jun 2026
Pages:
1-4
Publication Frequency:
Monthly (12 issues per year)
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.
Department of Computer Engineering
Sinhgad Academy of Engineering,Kondhwa BK,Pune
Department of Computer Engineering
Sinhgad Academy of Engineering,Kondhwa BK,Pune
Department of Computer Engineering
Sinhgad Academy of Engineering,Kondhwa BK,Pune
Department of Computer Engineering
Sinhgad Academy of Engineering,Kondhwa BK,Pune
Department of Computer Engineering
Sinhgad Academy of Engineering,Kondhwa BK,Pune
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
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.
[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.
Publication Date
6/17/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
Documents
Article Views
0
Total views since publication
PDF Downloads
8
Total downloads since publication
Geographic Reach
5
Digital Preservation: This article is permanently archived on Zenodo and complies with all data preservation standards across major indexing platforms.
Plagiarism Screening
This manuscript has been checked for plagiarism using TurnitinPlagiarismChecker software.
Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
Related research in the same subject area
Loading similar articles...