Optimising Lung Cancer Screening Through Advanced Techniques for Image Analysis: An Efficient Deep Learning Approach

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 C...

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Bibliographic Details
Published in2023 4th International Conference on Smart Electronics and Communication (ICOSEC) pp. 1528 - 1535
Main Authors Sherin, K., Jaspin, K., S, Harini, Sivanantham, Sakthivel, Senthilvelan, Surendar, Theerthagiri, Srinivasan
Format Conference Proceeding
LanguageEnglish
Published IEEE 20.09.2023
Subjects
Classification techniques
Computed tomography
Computer aided designing
Deep learning techniques
Image processing
Image segmentation
Lung
Lung cancer
Neural networks
Object recognition
Squamous cell carcinoma
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Summary: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%
DOI:10.1109/ICOSEC58147.2023.10275884