click here to download project abstract related to lung cancer detection using cnn
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ABSTRACT
we provide abstract related to lung cancer detection using cnn
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Introduction: In response to the growing need for advanced diagnostic tools in medical imaging, this research focuses on developing an innovative solution for the early detection of lung cancer. By employing CNN, the study aims to enhance the accuracy and speed of lung cancer classification, potentially leading to improved patient outcomes.
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Data Acquisition and Preprocessing: The research utilizes a diverse dataset of lung cancer images obtained from various medical sources. Rigorous preprocessing techniques are applied to ensure data quality, including normalization and augmentation, laying the foundation for robust model training.
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Convolutional Neural Network Architecture: The core of the proposed methodology lies in the design of a powerful CNN architecture tailored for lung cancer classification.
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Training and Validation: The CNN model undergoes extensive training using the preprocessed dataset, employing techniques such as cross-validation to ensure generalizability. The validation process assesses the model’s performance on unseen data, refining its ability to accurately classify diverse lung cancer cases.
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Performance Evaluation: The research evaluates the CNN’s performance in terms of sensitivity, specificity, and overall accuracy. Comparison with existing methods and benchmark datasets helps establish the efficacy of the proposed model in lung cancer detection.
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Conclusion: This study demonstrates the potential of Convolutional Neural Networks in significantly improving the accuracy of lung cancer classification.
