Musculoskeletal Abnormality Detection in Humerus Radiographs Using Deep Learning
Bibliographic record
Abstract
Musculoskeletal radiographs bring a considerable amount of meticulous expertise in treating Bone diseases (BDs) or injuries. Usually, less experienced doctors are the first ones for assessment of radiographs and it is not surprising for humerus disorders being misdiagnosed. To take care of such misdiagnosis, Deep Learning and Machine Learning could play a major role in diagnosis of the musculoskeletal abnormalities. The presented paper intends to develop a better performing Computer Based Diagnosis (CBDs) model. First, some preprocessing techniques are performed on the chosen dataset of humerus radiographs, eliminating image size variability from the radiographs. Next, two architectures namely-DenseNet201 and Inception V3 were used to classify the given dataset as abnormal or normal. Later, ensemble techniques are applied to improve model's performance. The proposed technique is tested for the publicly available Musculoskeletal Radiographs (MURA) dataset and the qualifier results are compared with present results from the reference paper. For humerus radiographs, the accuracy achieved is 88.54%. Implementation results show the proposed method is a deserving strategy to classify bone disorders.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".