Enhancing Lung and Colon Cancer Diagnosis: An ImageNet-Trained Transfer Learning Approach for Histopathological Image Analysis

This study presents a novel approach for lung and colon cancer diagnosis using VGG16 transfer learning applied to histopathological image analysis. Focusing on two prevalent cancers, our dataset comprised 25,000 images, with 5,000 images per class. We employed the VGG16 convolutional neural network, leveraging transfer learning to adapt the model to the unique characteristics of histopathological imaging. The research involved rigorous preprocessing to standardize and enhance image quality, ensuring the VGG16 model could effectively learn and differentiate various tissue patterns. The model's performance was evaluated using a range of metrics, yielding outstanding results: a ROC AUC Score of 0.9996456, and high scores in accuracy (0.986933), precision (0.986928), recall (0.986933), F1 score (0.986924), sensitivity (0.986933), and specificity (0.986928). These results demonstrate the model's exceptional ability in classifying both malignant and benign tissues accurately, indicating significant potential for aiding pathologists in early cancer detection and diagnosis.