Optimising Lung Cancer Screening Through Advanced Techniques for Image Analysis: An Efficient Deep Learning Approach
Kevin M. Sherin, K. Jaspin, Harini S, Sakthivel Sivanantham, Surendar Senthilvelan, Srinivasan Theerthagiri · 2023
The objective of this analysis is to identify and classify diverse types of lung cancer and propose an improved system to enhance the current best approach. The proposed system utilizes a preprocessed dataset to train and test various types of lung cancer, including Lung Adenocarcinoma, Lung Large Cell Carcinoma, and Squamous Cell Carcinoma, as well as a category for normal lungs. The accuracy of the detection methods is evaluated using a confusion matrix to ensure maximum accuracy. The detection methods for lung cancer are rated based on their accuracy in detecting the disease. This study emphasizes the significance in detecting abnormal lung cells indicative of cancer as expeditiously as possible to alleviate mortality rates and highlights the importance of computer-aided diagnosis in improving accuracy. The proposed approach achieved an impressive accuracy of 93.11%