Hybrid Residual Network and XGBoost Method for the Accurate Diagnosis of Lung Cancer

Lung cancer mortality is increasing in this country due to a variety of factors, including the country’s increasing industrialization, the buildup of hazardous substances in the environment, and an ageing population. Computed Tomography (CT) scans are routinely used on patients as part of the diagnostic procedure to achieve a conclusive diagnosis of lung cancer. Because of the way X-rays are absorbed by biological tissues, CT scans may detect even the most deeply hidden structures. Pulmonary nodules are lumps or bumps in the lungs that are signs of disease. Because each type of nodule can take many different shapes, the chance of getting cancer varies a lot. In some cases, computer vision models can now help clinicians identify a wide range of medical disorders; in some cases, these models have been found to be more accurate than actual doctors. Deep learning advancements in recent years have made this possible. The disease diagnosis carries a great deal of significance and value for the field due to the numerous opportunities provided by modern technology. This is because the application’s primary function is to serve as a diagnostic tool for a variety of illnesses. Our goals were to improve the accuracy of diagnostic tests and to find diseases earlier. We discovered that the model could detect lung cancer earlier than other approaches already in use. The suggested model includes the following elements: Identifying pulmonary nodules, discussing false positives, and discussing diagnostic uncertainty the number of lung nodules will be reduced by removing “false nodules” and classifying lung nodules as benign or malignant. Throughout human history, new network architectures and loss functions have been developed and implemented. Furthermore, the recommended deep learning mode could be improved, resulting in improved lung nodule identification accuracy. Experiments have shown that the proposed method greatly improves the ratio of accuracy to precision when evaluating the disease being studied.