Comparing Three Different Layers of CNN using Lungs Cancer Dataset

One of the most common cause of mortality worldwide is lung cancer, one of the deadliest cancers. As a result, early diagnosis is essential to save a human life. Deep learning has recently become a popular choice for disease detection and classification in studies. The categorization of lung cancer is done in this research utilizing CT images from the SPIE-AAPM-LungX collection. The CNN design is capable of classifying a huge dataset of medical pictures, and a sample dataset of 500 images from the SPIE-AAPM-LungX data collection was used to diagnose lungs cancer and a comparison was shown by modifying the layers of CNN architecture for the proposed system. When compared to three distinct layers of CNN, the proposed model scored the greatest test accuracy of up to 93.78%. The result was confirmed using the 10-fold cross-validation method.