Fuzzy Sematic Segmentation and Efficient Classification of Lung Cancer Multi-Dimensional Datasets

2. Literature Survey

As the ongoing advancements uses the vast image processing with machine learning, deep learning and fuzzy logic in the medical field this one has ignited light on the researchers to predict the lung cancer utilizing the traditional methods along with the curving developments.. Traditionally computerized tomography (CT) scan is preferred for the diagnosis purpose (Makaju S, et al 2018) of lung cancer and sometimes x-rays were also chosen but as currently using image processing techniques like image capturing image enhancement and image segmentation along with the fuzzy logic combination has become a new trend to achieve healthier precise results. This research paper is using neuro fuzzy logic combination for the research lying on prediction of lung cancer at an early stage of abnormality. In (Gong et al 2018) the authors have designed a system to predict the lungs cancer cell in the lung with the two different sources of input datasets; LUNA16 datasets with 888 number of CT scans with 1186 nodules and other datasets source from ANODE08, it includes 5 scans with no of 39 nodules. The model was essentially proposed to detect the dot like preliminary nodules in lungs. (Yu et. al 2018), (Li et. al 2020)