Enhanced Diagnosis of Lung and Colon Cancer Severity Through Deep Feature Analysis Using <scp>DenseNet201</scp> and <scp>SVM</scp> With Histopathological Images: A Super‐Resolution Approach

The implementation of the FSRCNN and SVM-based DenseNet201 model provided substantial improvements in the detection of lung and colon cancer, as evidenced by high accuracy and specificity metrics. While the LC25000 dataset offered a solid foundation for this analysis, future research should aim to validate the model's effectiveness using a broader array of diverse and extensive datasets. Additionally, integrating supplementary diagnostic techniques, such as genetic data and electronic health records, could further enhance the model's diagnostic precision and practical application in cancer diagnosis.