Leveraging U-Net 3+ for Improved Lung Cancer Segmentation and Diagnosis

In addressing the crucial problem of lung cancer segmentation in medical imaging, this research emphasises how crucial accuracy is to a proper diagnosis and efficient treatment planning. The U-Net3+ architecture, which is intricately connected with deep neural networks (DNNs), is highlighted along with its sophisticated, layered skip connection design. The methodology consists of a thorough analysis of the dataset, preprocessing methods, and U-Net3+'s architecture implementation. The outcomes show how effective the model is, as evidenced by its high degree of tumour boundary identification accuracy. U-Net3+'s accuracy in defining affected areas is clearly depicted visually, providing encouraging opportunities for better lung cancer diagnosis and treatment strategizing. In summary, the research highlights the advances made possible by U-Net3+ and adds to the changing field of lung cancer segmentation. Comprehensive evaluations and visual representations of its re-markable performance establish it as a promising tool in medical image analysis. The potential influence of U-Net3+ on improving the diagnosis and treatment planning of lung cancer points to a worthwhile direction for further investigation and clinical applications. The study analyses the literature and demonstrate U-Net3+'s superior performance with a Dice coefficient of 0.94 by contrasting it with modern models like MAU-Net, Tritention U-Net, and U-Det