Malignancy Detection in Lung and Colon Histopathology Images by Transfer Learning with Class Selective Image Processing

Due to its ferocity, enormous metastatic potential, and variability, cancer is responsible for a disproportionately high number of deaths. Cancers of the lung and colon are two of the most common forms of the disease in both sexes worldwide. The excellence of treatment and the endurance rate for cancer patients can be greatly improved with early and precise diagnosis.

We suggest a computationally efficient and highly accurate strategy for the rapid and precise diagnosis of lung and colon cancers as a substitute for the standard approaches now in use. The training and validation procedures in this work made use of an enormous dataset consisting of lung and colon histopathology pictures. There are 25,000 Histopathological Images (HIs) in the dataset, split evenly among 5 categories (mostly lung and colon tissues). Before training it on the dataset, a pretrained neural network (AlexNet) had its four layers fine-tuned.

The study enhances malignancy detection in lung and colon histopathology images by applying transfer learning with class-selective image processing. Instead of enhancing the entire dataset, a targeted contrast enrichment was applied to images from the underperforming class, improving the model's accuracy from 92.3% to 99.2% while reducing computational overhead. CONCLUSION: This approach stands out by emphasizing class-specific enhancements, leading to significant performance gains. The results meet or exceed established CAD metrics for breast cancer histological images, demonstrating the method's efficiency and effectiveness.