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Early diagnosis of lung cancer plays a pivotal role in ensuring improved treatment and survival of patients. This remains a major focus in clinical research. Artificial intelligence (AI) has transformed pathology by significantly improving diagnostic accuracy and efficiency. This study presents a robust deep learning model in the shape of the pretrained EfficientNetB7 model to classify colon and lung tissue histopathological images with an extremely high accuracy of 96%. The model's performance was optimized using advanced preprocessing methods, fine-tuning, and domain-specific data augmentation techniques. These strategies help reduce problems such as class imbalance and subtle histological variations. To address the issue of overfitting, multiple data augmentation techniques were combined, and an early stopping criterion was incorporated. This approach enabled efficient and cost-effective training. Robust validation of the model demonstrates high utility for clinical applications and enables pathologists to deliver timely and accurate diagnoses. Integrating advanced deep learning models into medical imaging workflows holds great promise for early and accurate cancer diagnosis, ultimately improving patient outcomes.