Classification and Diagnosis of Mammary Tumors in Dogs Using Deep Learning Techniques
Bibliographic record
Abstract
Mammary tumors are commonly found in domestic animals and significantly affect animal health.In this context, the analysis of mammary tumor cells in domestic animals is crucial for the diagnosis, monitoring, and treatment of cancer.These tumors can also lead to the death of animals.Therefore, academic studies are needed to prevent and early diagnose mammary cancer in animals.In this study, an artificial intelligence-supported system for early diagnosis of whether mammary tumor sections taken from domestic dogs will cause cancer was developed.The proposed system consists of image processing and classification stages.The data for the study was provided by the Faculty of Veterinary Medicine at Burdur Mehmet Akif Ersoy University.In the image processing stage, various data preprocessing techniques and deep learning-based convolutional neural networks were used.Then, benign and malignant mammary tumors were classified to diagnose cancer.Data preprocessing techniques such as data augmentation, normalization and image filtering techniques were used to improve the performance of the deep learning-based image processing algorithm.The Xception model achieved a 98.9% success rate in tumor diagnosis.The study is believed to have made a significant contribution to the literature by using current methods and having a unique data set.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".