01923nas a2200181   4500000000100000000000100001008004100002100001900043700001800062700002300080700001900103700001600122245008500138856004600223490000700269520145100276022001401727       2023                            d1 aMohamed Hammad1 aPawel Plawiak1 aMohammed ElAffendi1 aAhmed El-Latif1 aAsmaa Latif00aEnhanced Deep Learning Approach for Accurate Eczema and Psoriasis Skin Detection  uhttps://www.mdpi.com/1424-8220/23/16/72950 v233 a<p>This study presents an enhanced deep learning approach for the accurate detection of eczema and psoriasis skin conditions. Eczema and psoriasis are significant public health concerns that profoundly impact individuals’ quality of life. Early detection and diagnosis play a crucial role in improving treatment outcomes and reducing healthcare costs. Leveraging the potential of deep learning techniques, our proposed model, named “Derma Care,” addresses challenges faced by previous methods, including limited datasets and the need for the simultaneous detection of multiple skin diseases. We extensively evaluated “Derma Care” using a large and diverse dataset of skin images. Our approach achieves remarkable results with an accuracy of 96.20%, precision of 96%, recall of 95.70%, and F1-score of 95.80%. These outcomes outperform existing state-of-the-art methods, underscoring the effectiveness of our novel deep learning approach. Furthermore, our model demonstrates the capability to detect multiple skin diseases simultaneously, enhancing the efficiency and accuracy of dermatological diagnosis. To facilitate practical usage, we present a user-friendly mobile phone application based on our model. The findings of this study hold significant implications for dermatological diagnosis and the early detection of skin diseases, contributing to improved healthcare outcomes for individuals affected by eczema and psoriasis.</p>
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