Performance Analysis Of Cardiac Segmentation Using ACNN - S.shireesha, Dr.G.A.E.Satish kumar (International Journal of Advanced Science and Technology, 2020)
According to the study of WHO(World Health Organization) there is major cause of deaths globally due to cardiovascular diseases (CVD).Deep learning is one of the best domain used for cardic segmentation which can be used for different modalities like MRI,CT,PTE,etc.Proposed Framework is independent of many anatomical properties like shape , structure of labels of different human organs.The designed framework can be analyzed for different tasks such as image segmentation and image enhancement also,improves the prediction rate compare to state of art techniques.Before going for in depth analysis we must have prior knowledge of organs.Proposed algorithm uses semantic segmentation concepts by CNN(Convolution Neural Network) which is also known as ACNN(Anatomically Constrained Neural Networks).This method will enhance and segment the organ part from whole cardiac image. In existing different techniques(like NN,SVM,KNN) are used to segment organ part but they fail when the input image is noisy.Our proposed method will segment the organ from cardiac images with very high accuracy(CDR:Correct Detection Rate) and higher similarity index.With both subjective and objective analysis we shown that our method is more efficient than existing techniques.