Cardiovascular Disease Detection using Transfer Learning Approach

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dc.contributor.advisorDr. Shahnewaz Siddique
dc.contributor.authorJahangir Alam
dc.contributor.authorTashdid Alam
dc.contributor.authorHafsa Tasnim Badhan
dc.contributor.id1921863042
dc.contributor.id1931264042
dc.contributor.id1911406642
dc.date.accessioned2026-04-22
dc.date.accessioned2026-04-22T06:14:30Z
dc.date.available2026-04-22T06:14:30Z
dc.date.issued2024
dc.description.abstractCardiovascular Disease (CVD) is a multidimensional worldwide health concern, covering illnesses such as myocardial infarctions, cerebrovascular accidents and excessive blood pressure. Understanding its risk factors is crucial for successful preventative actions and therapies. This research is inspired by the astounding effect of CVD, responsible for 17.9 million deaths yearly, comprising 31% of global mortality [32]. Encouragingly, the American Heart Association says up to 80% of CVD cases are avoidable by lifestyle adjustments and early therapies. Proactive management of risk factors is crucial, with the healthcare sector, equipped with significant patient data, playing a critical role. The paper presents a comprehensive investigation into the development and evaluation of deep learning models for binary classification in cardiovascular disease detection. Four distinct models like ResNet50, EfficientNetB0, CNN and VGG16 were explored, each utilizing different architectures and transfer learning techniques. The study begins with a detailed description of data loading and preprocessing procedures, highlighting the importance of standardized image preparation techniques for effective model training. Each model's architecture is thoroughly discussed, elucidating the unique characteristics and design choices that contribute to their performance. The ResNet50 model employs residual blocks to address the vanishing gradient problem, while the EfficientNetB0 leverages transfer learning with pre-trained layers from ImageNet. The CNN model features a basic yet successful architecture with convolutional and dense layers, whereas the VGG16 model adopts a deep and uniform structure with 3x3 convolutional kernels [33]. Training procedures and results are meticulously documented for each model, encompassing aspects such as epoch-based training, batch sizes and optimizer selection. Performance metrics including accuracy and loss are analyzed across epochs to assess model convergence and generalization capabilities. Notably, the EfficientNetB7 model outperforms others, achieving remarkable accuracy and minimal loss on both training and validation datasets, demonstrating its efficacy in cardiovascular disease detection. The ResNet50 model achieved a training accuracy of 86.39% and a validation accuracy of 84.72%. The EfficientNetB0 model attained a training accuracy of 97.32% and a validation accuracy of 98.83%. The CNN model demonstrated a training accuracy of 80.55% and a validation accuracy of 87.83%. Finally, the VGG16 model achieved a training accuracy of 95.13% and a validation accuracy of 99.55%. The paper also presents the development of a user-friendly web application using Streamlit, facilitating easy access to the trained models for real-world application [34]. The application comprises instructional, classifier and about us pages, each designed with clarity and simplicity to enhance user experience. Sample images and immediate classification results contribute to the usability and trustworthiness of the application, catering to medical professionals' needs for accurate and timely diagnostics [34]. The study provides valuable insights into the application of deep learning models for cardiovascular disease detection, elucidating the significance of architecture selection, data preprocessing and training procedures. The findings underscore the efficacy of transfer learning techniques and model architectures in achieving high accuracy and reliability in medical image classification tasks. The developed web application offers a practical solution for real-world deployment, facilitating seamless integration of deep learning technologies into clinical workflows.
dc.description.degreeUndergraduate
dc.identifier.cd600000906
dc.identifier.print-thesisTo be assigned
dc.identifier.urihttps://repository.northsouth.edu/handle/123456789/1647
dc.language.isoen
dc.publisherNorth South University
dc.rights©NSU Library
dc.titleCardiovascular Disease Detection using Transfer Learning Approach
oaire.citation.endPage84
oaire.citation.startPage1
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