This paper introduces a new way to improve the accuracy and transparency of deep learning models for detecting skin diseases.It addresses the well-known "black box" issue of deep neural networks by integrating explainable artificial intelligence (XAI) techniqueswith image processing methods. The suggested framework begins with a preprocessing stage, where image processing techniques such asnoise reduction, contrast enhancement, and lesion segmentation are applied to skin lesion images. These techniques enhance the quality ofthe input data, thereby boosting the model's ability to extract relevant features. The processed images are then input into a convolutionalneural network (CNN) that is fine-tuned to classify different skin conditions. To ensure that the model's decisions are transparent, theframework incorporates XAI methods, such as Grad-CAM (Gradient-weighted Class Activation Mapping). Grad-CAM creates heatmapsthat highlight the specific parts of the image the model focuses on when making a prediction. This two-part strategy, which utilises imageprocessing to enhance input and XAI to clarify output, yields a more reliable and trustworthy system. Experimental results show that theproposed method not only achieves a high level of diagnostic accuracy but also gives clinicians a visual explanation of the model’sreasoning. This increased transparency is crucial for clinical use, as it enhances confidence and facilitates the integration of AI tools intodermatological practice