Lung and Colon Cancer Detection using Transfer Learning

Lung and colon most cancers are common and adverse sicknesses in modern-day society and pose tremendous fitness risks worldwide. Accurate detection of those cancers is essential for timely intervention and powerful treatment. In this venture, we developed a strong deep learning model using EfficientNetB3 architecture for accurate class of lung cancer and colon cancer histopathology photographs. Using a dataset of 25,000 photos, we done an incredible accuracy of 99%. Evaluation metrics including precision, recall and F1 scores, imply the effectiveness of the model in distinguishing among cancerous and non-cancerous tissues. Our method highlights the importance of superior device studying techniques in enhancing most cancers prognosis, paving the way for better patient care and results.