Abstract:
Among the deadliest cancers, lung cancer is very deadly, frequently having fatal results. Medical professionals face complex
challenges when using CT scan pictures to assess cancer. As a result, there is an urgent need to create computer-aided systems that
may reduce processing time and human labor while simultaneously properly detecting and classifying lung cancer. This research
proposal is dedicated to the pivotal task of lung nodule detection, classification into benign or malignant categories in early cancer
diagnosis, and subsequent precise segmentation of lung cancer nodules, offering insight into their spatial extent within the CT
images based on robust deep learning network. Extensive parameter optimization has been undertaken to craft an efficient
and powerful convolutional neural network (CNN) architecture. One of the standout features of this research is its ability
to not only accurately classify lung nodules but also precisely delineate their spatial boundaries, calculate their areas, and
pinpoint their exact locations within medical images. Our approach leverages a bespoke convolutional neural network (CNN)
architecture, meticulously fine-tuned through empirical experimentation, to achieve a remarkable accuracy rate of 99.17%, precision
rate of 99.01%, recall rate of 99.75%, and F1-score rate of 99.37%. This surpasses the performance of existing methods in the field
and marks a significant breakthrough in lung cancer detection and analysis. The results offer a promising step forward in early diagnosis
and treatment planning for lung cancer patients, ultimately contributing to enhanced patient care and outcomes.
