Enhanced Diagnosis Approach of Lung Cancer with Leveraged Deep Learning Models

The complex structure of lung cancer makes early detection and diagnosis difficult, even though they are critical for better patient outcomes. In this study, we delve into the realm of deep learning to enhance the diagnosis of lung cancer, leveraging CT scan images. We intend to determine the best effective strategy for early cancer detection by meticulously pre-processing the dataset and comparing multiple deep learning models, such as ResNet152V2, Inception V3, ANN, and FNN. The practical application and comparative study of these models are provided by our research, which adds to the field by illuminating their diagnostic accuracy and performance measures including Accuracy, Precision, Sensitivity, Specificity, and Area under the ROC curve. We also delve into the possibilities for building strong clinical decision support systems for early cancer detection, as well as the role of deep learning in medical imaging. While our study presents some interesting findings and discusses different aspects of technology in the realm of programming testing, it is acknowledged that further improvements are needed. Specifically, enhancing the presentation quality, providing more detailed explanations of the proposed algorithms, and incorporating substantial results or insights are recommended for future iterations of this research. Through continual refinement and exploration, we aim to contribute to the ongoing advancements in deep learning-based cancer detection, ultimately striving for improved patient outcomes and enhanced clinical decision-making.