Optimization of Neural Networks Using Mathematical Algorithms: An LSTM-Based Machine Learning Approach to Lung Cancer Diagnosis

The paper concentrates on boosting the precision and speed of neural networks for lung cancer diagnosis through novel optimization methodologies. This study employs Long Short-Term Memory neural networks specifically designed for time-series and sequential data, to introduces mathematical algorithms to improve the training process in a Neural Network. The goal is, therefore, to obtain more accurate diagnoses than with use of CXR alone and fewer computational costs hopefully even easier detection in the early stages. This model merges the optimisation mathematics that includes gradient descent, Adam optimiser and various ways of regularising a little to tune in LSTM layers well for better feature extraction out of patient data. The results have shown a remarkable superiority of the model performance compared to traditional diagnostic approaches including precision, sensitivity and specificity metrics. Using a unique combination of feature pre-processing methods and optimization, this paper shows how machine-learning based diagnosis can be made resilient to noisy data that might otherwise propagate errors into the clinical decision process. These results could increase robust lung cancer diagnostics by lowering the rates of false positives and negatives, while in turn improving patient management and treatment.