Lung Cancer Detection Using Deep Learning

Abstract

Lung cancer remains a significant global health concern, necessitating advanced and accurate detection methodologies. This study introduces two novel approaches for lung cancer detection, leveraging the power of Multi-Path Convolutional Neural Networks (CNN) and a Multi-Scale Multi-Path approach. The proposed methods aim to enhance the specificity and sensitivity of lung cancer detection compared to traditional CNN approaches. In the first approach, a multi-Path CNN architecture is employed to exploit diverse hierarchical features within the lung images. The effectiveness of this approach is evaluated through rigorous experimentation on a diverse dataset, demonstrating its superior performance in identifying lung cancer lesions. The second approach introduces a Multi-Scale Multi-Path strategy, combining the advantages of multi-scale analysis with the flexibility of a multi-path architecture. This approach allows for the simultaneous exploration of different scales, capturing both fine and coarse details present in lung images. The experimental results showcase the robustness of this method, revealing improved accuracy and reliability in lung cancer detection compared to conventional CNN architectures. To validate the proposed approaches, extensive comparative analyses are conducted against traditional CNN models using benchmark datasets. The evaluation metrics include sensitivity, specificity, precision, and overall accuracy. The results indicate that the Multi-Path CNN and Multi-Scale Multi-Path approaches outperform their counterparts, underscoring their potential as advanced tools for lung cancer detection. In conclusion, the proposed innovative methodologies harness the capabilities of Multi-Path CNN and Multi-Scale Multi-Path approaches, offering promising advancements in the field of lung cancer detection. newline