Most of the recent cancer classification methods use gene expression profile as features because it can provide very important information regarding tumor characteristics. Motivated by their success in the computer vision area now deep learning has been successfully applied to medical data because it can read non-linear patterns in a complex feature and can allow the leverage of information from unlabeled data of problems that do not belong to the problem being handled. In this paper, we implement transfer learning, which refers to the use of a model trained on one task to perform classification on another task to classify five cancer types that most commonly affect women. We used VGG16, Xception, DenseNet, and ResNet50 as base models and then added a dense layer to reflect our five-class classification problem. To avoid training over-fitting that can result in a very high training accuracy and a low cross-validation accuracy we used L2-regularization. We retrained (fine-tuned) these models using a five-fold cross-validation approach on RNA-Seq gene expression data after transforming it into 2D-image like data. We used the softmax activation function with the prediction dense layer and adam as optimizer in the model fit for all four architectures. The highest performance is obtained when fine-tuning Xception architecture, which achieved classification accuracy = 98.6%, precision = 98.6%, recall = 97.8%, and F1score = 98% on five-fold cross-validation training and testing approach.