Abstract: Lung cancer remains a significant global health challenge, demanding precise and timely diagnostic interventions for improved patient outcomes. This research proposes an innovative approach, the Optimized-ANN (Artificial Neural Network) method, to improve the precision oflung cancer diagnostic through the integration of machine learning techniques. By optimizing the architecture and parameters of the ANN, we aim to achieve superior diagnostic precision, aiding clinicians in early detection and tailored treatment planning. The Optimized-ANN methodology involves a multi-step process, encompassing preprocessing of medical imaging data, Principal component analysis (PCA) for dimensionality reduction and feature extraction, hyperparameter optimization, and construction of a customized ANN. The resulting model is trained and validated using a diverse dataset, with a focus on robustness and generalization to various patient profiles. Our research adds to the corpus of knowledge by providing a thorough and refined method of diagnosing lung cancer. The evaluation Metrics like F1-score, recall, accuracy, and precision providea detailed understanding of the design's performance. Furthermore, cross-validation ensures the reliability of the Optimized-ANN across distinct subsets of the dataset. The anticipated outcomes of this research include heightened diagnostic accuracy, efficient feature representation, and adaptability to diverse imaging conditions. As lung cancer diagnosis relies heavily on medical imaging, the Optimized-ANN Approach holds the potential to significantly impact clinical decision-making, facilitating earlier interventions and ultimately improving patient prognosis. This paper sets the stage for the detailed exploration of the Optimized-ANN Approach, underscoring its potential as a valuable tool in the realm of lung cancer diagnosis and contributing to the broader landscape of machine learning applications in healthcare.
Authors: R. Balamanigandan, R Mahaveerakannan, Pankaj Dadheech, R. Bhavani, R. Dhanalakshmi
DOI: https://doi.org/10.1145/3647444.3652448
Publish Year: 2023
