Lung Cancer Classification with Improvised Three Parameter Logistic Type Distribution Model

Abstract

Lung cancer is the leading cause of mortality worldwide, affecting both men and women equally. Identifying and treating these nodules when they are still tiny may increase their chances of survival significantly. However, due to the large amount of data generated by this CT scanner, manual segmentation and interpretation takes a long time and is quite challenging to do on your own. When a radiologist focuses on the patient's body, it increases the strain on the radiologist, and the likelihood of missing pathological information, such as abnormalities, is also increased. One of the primary objectives of this project is to develop computer-assisted diagnosis and detection of lung cancer. It also intends to make it easier for radiologists to identify and diagnose lung cancer more rapidly and accurately. Based on a unique picture feature, the proposed strategy k into consideration the spatial interaction of voxels that were next to one another. Using the U-NET+Three parameter logistic distribution-based technique, we were able to replicate the situation. According to the researchers, the proposed technique method DSC of 97.3%, a sensitivity of 96.5%, and a specificity of 94.1% when tested on the LuNa-16 dataset. At long last, this research investigates how diverse lung segmentation, juxta pleural nodule inclusion, and pulmonary nodule segmentation approaches may be applied to create CAD systems. Other objectives include making it possible to conduct research into lung segmentation and automated pulmonary nodule segmentation while also improving the power and effectiveness of computer-assisted diagnosis of lung cancer, which relies on correct pulmonary nodule segmentation to be successful.

An improved V-Net lung nodule segmentation model based on pixel threshold separation and attention mechanism

“…Finally, the DSC coefficients of the model on the public datasets FUSCC and LUNA16 reach 89.29% and 86.496%, respectively, and the model has strong robustness. Joshua et al 20 used the logical distribution model of three parameters in the feature extraction process and used the U-Net network structure as the benchmark model. The scheme achieved excellent results in the segmentation of pulmonary nodules.…”