Automated Facial Skin Disease Classification via Deep Learning: Enhancing Diagnostic Accuracy

Authors

  • Sekhar Pranitha
  • K Mahesh Babu
  • P M Priyanka
  • Kota Lakshmi Prasanna
  • Satri Tabita
  • Raga Chandrika P

Keywords:

Facial skin diseases, ResNet50 V2, deep learning, disease classification, image processing, accuracy, patient care, health management, early detection, medical image analysis, data augmentation, normalization

Abstract

Facial skin conditions present major obstacles to public health, requiring precise and prompt diagnosis for successful treatment. This study introduces a technique utilizing ResNet50 V2 based on deep learning to categorize different types of facial skin conditions. Utilizing a thorough dataset and implementing advanced image processing methods like data augmentation and normalization results in the model attaining high accuracy and robustness. ResNet50 V2 shows better precision, recall, and efficiency than traditional classification methods. Our method is verified by comprehensive experiments that demonstrate encouraging outcomes in the early identification and treatment of skin ailments. This advanced deep learning model provides a valuable tool for healthcare professionals, improving diagnostic accuracy and enhancing patient care outcomes. The study adds to the field of medical image analysis by offering a scalable and efficient method for classifying diseases.

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References

Nature Reviews Endocrinology, 2017, (A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, S. Thrun)

Dermatology, 2019, (Tschandl P, Rosendahl C, Kittler H)

IEEE Transactions on Medical Imaging, 2018, (Zhang J, Xie Y, Xia Y, Shen C, Shen C, Bailey J, Sakanashi H, Sugisaki K)

Computers in Biology and Medicine, 2019, (Menegola A, Tavares JMRS, Fornaciali M, Frade MAC)

Journal of the American Medical Association Dermatology, 2018, (Haenssle HA, Fink C, Schneiderbauer R, Toberer F, Buhl T, Blum A, Kalloo A, Hassen ABH, Thomas L, Enk AH, Uhlmann L)

International Journal of Environmental Research and Public Health, 2020, (Ferreira A, Matos S, Ferreira CM, Mendes JJ)

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Published

2025-05-05

How to Cite

1.
Pranitha S, Babu KM, Priyanka PM, Prasanna KL, Tabita S, Chandrika P R. Automated Facial Skin Disease Classification via Deep Learning: Enhancing Diagnostic Accuracy. J Neonatal Surg [Internet]. 2025 May 5 [cited 2026 Apr. 1];14(20S):825-9. Available from: https://jneonatalsurg.com/index.php/jns/article/view/5128

Issue

Vol. 14 No. 20S (2025): Journal of Neonatal Surgery

Section

Original Article

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