Machine Learning-driven Cardiovascular Disorder Diagnosis Through CT-imaging
Aindala Vishal, Nerella Vinod , Vinuthna Mankala, Kulkarni Vishal, Kovuru Vishal, Pallapu Vishnu
Machine Learning, ANN, SVM, Cardiac Image Segmentation, Analysis, Diagnosis.
Cardiac imaging plays a predominant role in the diagnosis of cardiovascular disorders. The main aim of this project is to diagnose cardiac disorders using CT imaging along with a machine learning technique (Artificial Neural Network). Image processing techniques such as preprocessing, segmentation, and classification are used for processing the image. Here segmentation and classification of the CT image play an important role in diagnosing the disorder, for segmentation, ANN (Artificial Neural Network) is being used and for classification, SVM (Support Vector Machine) is employed both come under the machine learning techniques. Implementing machine learning techniques emerges as the artificial intelligence tool that will be of service to the diagnosis of cardiovascular diseases. By constructing different algorithms for each process we can obtain precise and automated output. Thus, the output of the experiment helps the clinician to diagnose the cardiac disorders more clearly and can be moved to further treatment. Overall, this review provides a comprehensive overview of the current state and future potential of machine learning in cardiovascular imaging, highlighting its significant impact on improving the diagnosis and treatment of cardiovascular diseases.
Article Details
Unique Paper ID: 162038

Publication Volume & Issue: Volume 10, Issue 7

Page(s): 283 - 285
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