Introduction: One of the most popular applications of Artificial Intelligence that has seen immense growth in the digital era is Deep Learning techniques where the system studies and improves its performance through progressive learning without any explicit programming. Deep learning is widely used in numerous applications and one of them being medical analysis. Feature extraction and Image classification are considered to be the most popularly used approaches done using the deep learning process. Method: In this paper, we will segment cardiac bi-ventricle from magnetic resonance (MR) images. The segmented images are then classified through Deep Neural Networks where the sequence of images is validated frame by frame. The efficiency of the proposed model is evaluated and is compared with other traditional Deep Learning processes. Result: Theexecutionof the model is more precise as the model uses an iterative approach for feature extraction in classifying images. Conclusion: It is observed that the proposed interactive model provides better performance.