In this paper, a deep convolutional neural network (CNN) is proposed for accurate segmentation of retinal blood vessels. This method plays a significant role in observing many eye diseases. A strided-CNN model is proposed for accurate segmentation of retinal vessels especially the tiny vessels. The model is a fully convolutional model consisting of an encoder part and a decoder part where the pooling layers are replaced with strided convolutional layers. The strided convolutional layers proved to be better than the pooling layers because they are trainable. The morphological mappings along with the Principal Component Analysis (PCA)-based pre-processing steps are used to achieve well contrast images for training data. Skip connections concatenate features from the encoder part and the decoder part to enhance the vessels segmentation especially the tiny vessels and to make the vessel’s edges sharper. We used a class balancing loss function to train and optimize the proposed model to get better vessels image. The impact of the proposed segmentation method is evaluated on four databases namely DRIVE, STARE, CHASE-DB1, and HRF. The proposed method gives a sensitivity of 0.87, 0.808, 0.886, and 0.829 on DRIVE, STARE, CHASE DB1, and HRF respectively, along with an accuracy of 0.956, 0.954, 0.976, and 0.962 respectively. We focused on the sensitivity and the accuracy metrics which represent the accuracy of the model, especially the tiny vessels. According to the results, the model outperforms the other proposed methods,especially in the above-mentioned metrics.