Strategic Partnerships and Investment Opportunities in Deep Learning Applications for Medical Imaging

Li Jin
School of Medicine and Information Engineering, Anhui University of Chinese Medicine, Hefei 230012, Anhui China
Huanqing Xu
School of Electrical and Information Engineering, Tianjin University,Tianjin 300110, Tianjin China
Hongxing Kan
School of Medicine and Information Engineering, Anhui University of Chinese Medicine, Hefei 230012, Anhui China
Haimei Lu
School of Basic Medical Sciences, Anhui Medical University, Hefei 230032, Anhui China

DOI:https://doi.org/10.5912/jcb1146

Abstract:

Lung cancer remains a leading cause of morbidity and mortality globally, with early detection through lung nodule identification being critical for improving survival rates. Advanced image segmentation technologies, such as U-net++, have shown significant promise in enhancing the accuracy of these early diagnostics. This paper evaluates the application of U-net++ in lung cancer image segmentation, using a publicly available dataset to demonstrate its superiority over traditional methods in terms of performance metrics such as Dice coefficient, Intersection over Union (IoU), and Hausdorff Distance (HD). The broader acceptance of U-net++ across various clinical imaging modalities—from CT scans and MRIs to X-rays and microscopy—underscores its potential. However, the focus of this study extends beyond the technical validation, exploring how strategic partnerships and investments in deep learning technologies like U-net++ can facilitate their integration into healthcare systems. This includes a discussion on the commercial viability of U-net++, emphasizing how these technologies can attract investments, foster innovative business models, and potentially revolutionize medical imaging industries. The analysis aims to bridge the gap between technological advancements and commercial strategies, suggesting a framework for biotechnology firms to capitalize on deep learning innovations in medical diagnostics.