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Conclusion
=> Comprehensive Data Collection: This research introduced a robust method for capturing data to intelligently classify histopathological images. Collected a wide range of colorectal cancer histopathology images encompassing.
=> Future Implications: Research should extend transfer learning to tasks like tumor segmentation and lymph node detection in colorectal cancer diagnosis. Exploring advanced transfer learning techniques like fine-tuning and multi-task learning could further enhance model performance.
=> Histopathology Specifics: Recognizing the distinctive nature of histopathological images, highlighted the need for deep learning models to account for their unique features.
=> Model Evaluation: Five different deep learning classification models employed to evaluate the dataset. Among these, the Resnet50V2 model demonstrated highest classification accuracy, achieving an impressive rate of 95.00%.
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IEEE/ICACT20240405 Slide.17
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