Misdiagnosis of dermatological disorders is a common issue among healthcare professionals worldwide, especially when distinguishing between conditions with visually similar presentations. Ringworm (Tinea) and eczema are two skin disorders that are frequently misdiagnosed, leading to inappropriate treatments and potential complications. This study proposes a deep learning-based approach to enhance the differential diagnosis of these conditions using advanced convolutional neural networks (CNNs). Five pre-trained CNN architectures, such as VGG16, ResNet50, DenseNet121, InceptionV3, and EfficientNetB0, were fine-tuned and evaluated on the DermNet dataset, focusing on classifying ringworm and eczema. To improve model generalization, various data augmentation techniques were applied during training. Among the evaluated models, DenseNet121 demonstrated superior performance, achieving the highest classification accuracy. This model's effectiveness highlights its potential to significantly reduce misdiagnosis rates in dermatology. The results suggest that deploying CNN-based diagnostic tools could lead to more accurate and efficient dermatological assessments, improving both diagnosis precision and treatment outcomes. These findings pave the way for further research into AI-assisted healthcare solutions aimed at addressing diagnostic challenges in dermatology.