Early Lung Cancer Prediction by AI - Inspired Algorithm

• Published in: IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics (Online) Vol. 10; pp. 1466 - 1469
• Main Authors: Rao, K. B. V. Brahma, Bhardwaj, Yashi, Rao, G. V. Eswara, Gurrala, Jagadish, Jain, Arpit, Gupta, Keshav
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2023
• Subjects: Adaptation models; ANN; Computational modeling; Feature extraction; KNN; Logistic Regression; Lung cancer; Machine learning algorithms; Prediction algorithms; Predictive models; Random Forest
• ISSN: 2687-7767
• DOI: 10.1109/UPCON59197.2023.10434702

One of the top causes of cancer-related fatalities globally continues to be lung cancer. Recent advancements in medical imaging and image processing techniques have opened new avenues for improving the diagnosis and prediction of lung cancer. In this research, we propose a novel approach to predict the presence of lung cancer through the application of advanced image processing methods on lung radiographic images. To build an accurate prediction model, proposed machine learning algorithm was employed to examine the extracted features. The results of our study demonstrated promising outcomes, with the prediction model achieving a high accuracy, sensitivity, and specificity in distinguishing between lung cancer and benign cases. The proposed image processing techniques effectively highlighted subtle structural anomalies and patterns characteristic of lung cancer, aiding in early detection and timely intervention. Moreover, the techniques explored in this study could be adapted and extended to predict other types of cancer and contribute to the broader field of medical image analysis. Our proposed algorithm outperforms better accuracy than previous classifiers.