Treffer: Advanced lung tumor segmentation using deep learning-based method with the TDyWT algorithm.

Lung infections can be accurately classified from histopathological pictures, which is essential for oncology research, diagnosis, and therapy planning. In this work, region-based, edge-based, and deep-based UNet segmentation algorithms are used to improve lung tumor segmentation from histopathology pictures. According to our investigation, deep-based UNet segmentation regularly performs better than other techniques, showing improvements in F1 score, Sensitivity, and Intersection over Union (IoU). Furthermore, the Transverse Dyadic Wavelet Transform (TDyWT) is used to tackle the problem of the noise reduction process. The outcomes of the experiment shows the effectiveness of TDyWT and deep learning techniques in enhancing the precision and robustness of lung tumor segmentation from histopathology pictures. The Lung Cancer Histopathological Images dataset, available on Kaggle, was used for the studies. Python was used to construct segmentation techniques and noise reduction algorithms. The results of this study may improve patient outcomes, therapy planning, and diagnostic precision in the management of lung cancer. [ABSTRACT FROM AUTHOR]

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