Authors - N.Janani Abstract - In clinical practices, almost 18-20% cases go either unnoticed or misdiagnosed due to overlapping and subtle features in imaging, especially in complicated cases. We tackle this by using Cross-Modality Attention Network (CMAN) which integrates details from multi-phase CTs. By leveraging the distinct advantages of each scan phase, this method provides a comprehensive understanding of the tumor’s structure and characteristics. The cross-modality approach employs an attention mechanism to integrate information from multiple scan modalities, each capturing unique details. This process emphasizes the most critical tumor-related features while effectively minimizing noise, ensuring enhanced classification accuracy. Achieving an impressive accuracy of 98.47% on the LIDC-IDRI dataset, the CMAN significantly reduces misdiagnosis in complex cases. This approach can be really helpful in filling the diagnostic gaps, facilitating more informed clinical decision-making and improved patient outcomes.
Open Zoom
