An Innovative Diagnostic Framework for Lung Cancer Detection

The disease known as cancer is typified by abnormal cell proliferation that spreads throughout the body.In the lungs, abnormal cell growth leads to lung cancer.The lungs, the body's main respiratory control system, ensure that oxygen reaches every part of the body.It purifies the air and prevents infections and unwanted substances from entering the body.According to our immune system, every organ can battle inflammation and infections.Sometimes, though, they fall short in the fight against these infections, inflammations, and even malignant cells.This will inevitably lead to the development of cancer.Stages 0 through 4 are used to classify lung cancer.Early detection of lung cancer, either stage 0 or stage 1, increases a patient's chances of survival.If the cancer is found in its advanced stages, the chances of survival are quite low.Early identification of breast cancer is therefore essential.Many medical diagnostic methods, including Xrays and lung cancer screening, are available for the prediction of lung cancer.However, there are instances in which these diagnostic methods result in false positives or false negatives, requiring patients to get needless medical care.To avoid these outcomes linked to lung cancer projections, alternative approaches are needed.Even while there are other computerised methods for predicting lung cancer, they are also not very accurate.Therefore, using the lung cancer patient databases from Kaggle, we have created three models-Kernel Optimised Neural Network (KONN), Hierarchical Optimisation Neural Network (HONN), and Neural Adaptive Transformer Optimiser classifier method (NATO)-to predict lung cancer in its early stages.Along with the suggested efforts, the dataset is pre-processed using SMOTE to address the issues of class imbalance.Together with the training time for each suggested model, the performance of these methods is evaluated using the following metrics: accuracy, precision, and recall.When compared to the other two suggested models, such as Kernal Optimised Neural Network and Hierarchical Optimisation Neural Network, the Neural Adaptive Transformer Optimiser classifier approach in this research gives better accuracy and requires less training time.