Colon and Lung Cancer Classification of Histopathological Images Using Efficientnetb7

The computer vision task of classifying photos of lung and colon cancer tissue samples entails recognizing and separating the two types of cancer. The deep learning model EfficientNetB7 has demonstrated encouraging outcomes in a range of image classification applications, including medical picture analysis. In this proposed model used a collection of lung and colon cancer photos to properly categorize them using the EfficientNetB7 model. The results showed that the EfficientNetB7 model correctly categorized images of lung and colon cancer with an overall accuracy of 98.49%. The model also demonstrated outstanding accuracy, recall, precision, and F1-score, demonstrating its durability and capacity to generalize effectively on fresh and untested data. The findings show that EfficientNetB7 has the potential to provide accurate and timely diagnoses of lung and colon cancer, which might result in earlier identification and better patient outcomes.