Framework based on Machine Learning for Lung Cancer Prognosis with Big Data-Driven
DOI:
https://doi.org/10.47672/ejt.2787Abstract
Purpose: Lung cancer represents a fatal condition, which is typified by unregulated cell proliferation within lung tissues, which may be identified with the help of CT scans and X-rays that indicate tumors or aberrant masses. Medical imaging is essential in early diagnosis, which can enhance prognosis and the determination of an effective treatment approach.
Materials and Methods: The study presents an IQ-OTH/NCCD machine learning framework driven by Big Data to predict and prognose lung cancer based on 1,097 expert-labeled CT scan images in the dataset that runs under benign, malignant, and normal classes. The proposed system uses the preprocessing functions such as image shuffling, lung contour cropping, and resizing to utilize the inputs in the best way to train the model. To classify it, the EfficientNet-B1 is a deep learning model, a better model in accuracy and efficiency on computation thanks to the compound scaling depth, width, resolution.
Finding: Accuracy, precision, recall, and F1-score are the main performance indicators used to assess the model. The outstanding percentages of 99.10% accuracy, 99.22% precision, 97.22% recall, and 98.16% F1-scores demonstrate the model's exceptional performance. The model shows a massive improvement compared to classical models of machine learning, such as SVM or CNN. This system provides an efficient and scalable automated detection of lung cancer, thus facilitating smart healthcare with early detection and positive patient outcomes.
Unique Contribution to Theory, Practice and Policy: Future work requires a larger, multimodal dataset incorporating clinical and genetic data. Explainable AI methods should be explored to enhance generalizability. Real-world testing in smart healthcare settings and across multiple institutions is crucial for developing a practical, AI-driven tool for early lung cancer detection.
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Copyright (c) 2025 Raghuvaran Kendyala, Jagan Kurma, Jaya Vardhani Mamidala, Sunil Jacob Enokkaren, Avinash Attipalli, Varun Bitkuri
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