Enhancing Histopathological Image Classification through InceptionResNetV2: A Deep Learning Approach

Histopathological image analysis is a critical task in medical diagnostics, aiding in the accurate classification and staging of diseases. Traditional image analysis methods, while effective, often lack the precision and scalability provided by modern deep learning techniques. This study presents a sophisticated use of the InceptionResNetV2 architecture that has been optimized for the categorization of histological pictures from the colon and lungs. The InceptionResNetV2 model was trained on a comprehensive dataset of histopathological images of lung and colon cancer, which were pre-processed and augmented to enhance their quality and diversity. The model was adjusted to operate without the top layer and included additional dense layers with dropout for regularization to prevent overfitting. A custom learning rate adjustment callback was implemented during training to optimize key performance metrics. The final model achieved remarkable classification accuracy, with precision and recall rates exceeding 99%. This performance underscores the capability of InceptionResNetV2 in handling complex image features associated with histopathological data. The research showed that deep learning might significantly increase medical image analysis's prediction accuracy and efficiency, providing useful assistance for clinical decision-making. The results imply that these models may be expanded to include more medical imaging and diagnostic processes, offering a scalable solution for global healthcare systems.