Investigations on Deep Learning Techniques for Analysing Mammograms
Bibliographic record
Abstract
Mammograms have been acknowledged as one of the most reliable screening tools as well as a key diagnostic mechanism for early breast cancer detection. Though mammography is a valuable screening tool for detecting malignant growth in breasts, its competence as a diagnostic tool is heavily reliant on the radiologists’ understanding. Automated systems are now widely used for detection of breast cancer. Image processing techniques were widely used in automated systems for classifying mammograms. Of late with the advent of deep learning (DL) where images can be processed directly for classification, the DL is widely researched for medical image classification. Basically, DL techniques are representation-learning methods which aid in understanding data like sounds, images as well as texts. DL algorithms have the ability to learn multiple levels of representation as well as abstraction. Residual network (ResNet) is given due consideration as a kind of highly advanced Convolutional Neural Networks (CNNs). This work has offered a potential application of Visual Geometry Group (VGG), Residual network (ResNet) and Inception based CNN model for differentiating the mammograms into the abnormal class and the normal class. Experimental results demonstrated that the deep learners are effective for classifying mammograms and Inception deep learner achieved the best accuracy of 91.49%.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".