Abstract

Lung cancer is a type of cancer that starts in the lungs. It can cause different symptoms, such as coughing, chest pain, shortness of breath, and weight loss. The objective of this study is to develop and validate a robust and reliable predictive model that can accurately differentiate between small cell lung cancer and pneumonia based on medical imaging data, such as chest X-rays or CT scans. The data collection phase is to identify reliable sources of medical images that encompass both pneumonia cases and healthy individuals without pneumonia. From the collected dataset, the image is pre-processed using Principal Component Analysis (PCA) which is a transformation technique that reduces the size of the p-dimensional dataset. A sophisticated approach is adopted, combining t-distributed stochastic neighbour embedding (t-SNE) and an Ant Lion-Based Autoencoder (ALBAE) technique. The findings of the proposed technique, however, achieved the highest accuracy of 99 %. The study seeks to generate an integrated framework for the diagnosis and risk assessment of lung cancer and pneumonia using Python software. The future scope lies in further refining and optimizing the deep learning models to improve their accuracy and reliability in clinical settings.

References

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