Colorectal cancer classification based on histology images: comparison between DNN and CNN
Bibliographic record
Abstract
According to statistics from the World Health Organization, Colorectal Cancer (CRC) is the third most commonly diagnosed cancer in the world. The detection of CRC in an early stage is crucial for on-time and proper treatment, which may significantly increase the patient's survival rate. Although computers are not qualified to replace human experts at the moment, having a referential result from CRC auto-detection and saving the time of manual diagnosis is still very meaningful. This paper compares the performances of two different neural networks classifying CRC based on a set of histology images. The labeled dataset is publicly available on the Tensorflow website, and the two neural networks are tested on the same dataset separately. The first type of neural network in this study is Convolutional Neural Network (CNN), and the second type is a Deep Neural Network (DNN). As the dataset splits into training, testing, and validation sets, the loss, accuracy, and training time are recorded by the end of each epoch. The study result shows that the CNN method is better than the DNN method in terms of CRC image classification. It takes a long time but has better performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".