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In recent years, semantic segmentation with Deep Learning (DL) is popularly foundin many real-world applications. It is also specific, mentioned that these techniques are regularly applied to various segmentation and classification techniques in the medical field. The most popular deep learning techniques, especially SegNet and U-Net, are the most used for this type of medical application.Generally, in case of U-Net architecture which can be used with a skip connection and is capable of retrieving fine data during training. However, such a network consumes a lot of computation time compared to other networks. But SegNet is another network used to retrieve the desired information with a computing efficient. Inspired by the work, the skip connection is introduced into SegNet using the residual neural network (ResNet). ResNet consists of a layer and it has taken inputs involving multiple layers of the neural network, giving precise performance. This article offers first a recurrent convolutional neural network (RecNet) based on Seg-Net called R-SegNet and also a recurrent residual convolutional neural network (R2Net) based on SegNet models called R2-SegNet respectively. The strength of SegNet, RecNet and ResNet are used and have produced the architectures to perform the segmentation of cardiac MRI images. There are a number of advantages derived from the proposed architecture. First of all, using a residual unit, proposed architectures were used to carry out training in deep architecture. Second, the use of recurrent residual convolutional layers ensures that the relevant features retrieved to perform the segmentation tasks. Third, the proposed architecture has designed a good SegNet with a limited network parameters and also produce better performance for performing the task of segmentation in cardiac images. In addition, then, applied the level definition method, to extract the contours or surfaces of the cardiac MRI images.The results proves that the hybrid proposed deep learning methods successfully segments the images and also achieves better accuracy compared to standard architectures.