Psoriasis, a chronic autoimmune skin condition, is an abnormal proliferation of skin cells along with scaling and inflammation. Proper detection and identification of psoriasis early on are critical to avoid inappropriate treatment and management. Traditional methods of diagnosis heavily rely on prolonged and subjective to physician experience of clinical presentation and histopathological study. Deep learning algorithms have proved highly successful for medical image analysis in the past few years and offer computerized, accurate, and quick diagnosis features. Here, the use of convolutional neural networks (CNNs) is explored for classifying and detecting psoriasis from dermatology images. In the current research, a pre-trained deep learning models that are ResNet50, MobileNetV2, and EfficientNet-B0 with SGDM, and DenseNet201, with ADAM optimization techniques have been employed. In this chapter, a curated dataset of skin images affected with psoriasis was curated, in order to discriminate psoriatic lesions. To increase the accuracy, dataset is augmented with various image processing techniques. In the training, the experimental results demonstrate that the developed model is very accurate, sensitive, and specific. Our work suggests that deep learning models might be employed as valuable diagnostic aids, reducing errors in diagnosis and improved patient outcomes. Future studies will focus on enhancing model explainability and increasing datasets with varied skin types and disease severities.