Lung cancer is a leading cause of cancer-related problems worldwide, and its early diagnosis is helpful for improving patient results. This paper provides an overview of an innovative approach to lung cancer diagnosis using Artificial Intelligence (AI). The proposed system integrates AI technologies, such as deep learning and medical image analysis, to enhance the accuracy and efficiency of lung cancer detection. It focuses on the detection of lung cancer through a multi-stage approach. Initially, the widely used VGG-16 and VGG-19 convolutional neural network architectures are employed to establish a baseline for lung cancer detection. Performance metrics such as accuracy, precision, recall, and F1 score are evaluated to gauge the effectiveness of these pre-trained models. Subsequently, a novel modified convolutional neural network architecture is developed to enhance the accuracy and reduce false positives and false negatives in lung cancer detection. The modified architecture takes advantage of the unique characteristics of lung cancer imagery, incorporating features that exploit subtle patterns and anomalies often associated with this disease. A comparative analysis between the VGG models and the custom architecture is conducted, allowing for the identification of areas for improvement. The goal is to advance the accuracy and effectiveness of lung cancer detection, potentially contributing to early diagnosis and improved patient outcomes in the field of medical imaging and diagnostics. It is found that Modified CNN model produces more accuracy of 93.4% and the model is performing it’s best in all the other performance metrics too.
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