Abstract
Abstract: A prevalent bodily issue that has a significant impact on people's lives is skin disease. An early and precise identification of the condition can assist patients in receiving proper care, hastening their recovery. Convolutional neural networks (CNN) powered by deep learning have recently made major advancements that have greatly increased the accuracy of illness categorization. This led to the motivation for this work, which used deep CNN architectures to identify two different skin conditions—eczema and psoriasis. Ten fold cross validation has been utilized to examine the performance of five distinct cutting-edge CNN architectures. With the Adam optimizer, the Inception ResNet v2 architecture achieves a maximum validation accuracy of 97.1%. The results of the performance matrices suggest that the model performs remarkably well in terms of diagnosing skin disorders. Additionally, the study demonstrates two approaches for the practical application of the implemented model. (i) CNN Approach ii) PCA Approach.
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