International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Automatic Detection of Psoriasis Using Advanced Image Processing and Deep Learning

Author(s) NABHAN TAWJIH YOUSEF, RAGHIDA GHASSAN KASEM
Country India
Abstract Psoriasis is a chronic autoimmune condition affecting millions worldwide, characterized by red, scaly patches on the skin. Early and accurate detection is crucial for effective management and improved patient outcomes. This study presents a novel approach to automatic psoriasis detection using advanced image processing techniques and deep learning algorithms. We developed a convolutional neural network (CNN) model trained on a diverse dataset of 10,000 dermatological images, including both psoriatic and non-psoriatic skin samples. Our methodology incorporates state-of-the-art image preprocessing techniques to enhance feature extraction and segmentation. The proposed model achieved an accuracy of 95.3%, sensitivity of 94.8%, and specificity of 95.7% in identifying psoriatic lesions, outperforming existing automated methods. These results suggest that our approach could serve as a valuable tool for dermatologists, potentially expediting diagnosis and improving the efficiency of psoriasis management. The integration of this technology into clinical practice could lead to earlier interventions, personalized treatment plans, and ultimately, better patient care in the field of dermatology.
Keywords Psoriasis, Deep Learning, Image Processing, Automated Detection, Medical Images
Field Medical / Pharmacy
Published In Volume 7, Issue 1, January-February 2025
Published On 2025-01-04
Cite This Automatic Detection of Psoriasis Using Advanced Image Processing and Deep Learning - NABHAN TAWJIH YOUSEF, RAGHIDA GHASSAN KASEM - IJFMR Volume 7, Issue 1, January-February 2025. DOI 10.36948/ijfmr.2025.v07i01.34603
DOI https://doi.org/10.36948/ijfmr.2025.v07i01.34603
Short DOI https://doi.org/g82hgb
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E-ISSN 2582-2160
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