Fetal Heart Abnormality Detection in Prior Stage Using LeNet 20 Deep Learning Architecture
Bibliographic record
Abstract
Heart abnormalities are significant in medical diagnosis, traditionally detected through CT, X-ray, CTA, and MRI scans.However, these methods often yield inconclusive or erroneous results, leading to ineffective clinical recommendations.This study focuses on using ultrasound heart data for fetal anomaly prediction and classification, aiming to overcome the limitations of existing diagnostic methods.The purpose of this investigation is to develop a more reliable method for detecting fetal heart anomalies using deep learning techniques, specifically leveraging the LeNet 20 architecture.The goal is to improve the accuracy and reliability of fetal anomaly detection compared to conventional methods.Real-time fetal ultrasound heart samples were collected from NIMS super specialty hospital, Hyderabad, and pre-processed using tools such as Otsu threshold separation.The LeNet 20 convolutional neural network, consisting of 165 layers with max pooling, dense, hidden, and ReLU layers, was implemented using Python with TensorFlow, Keras, and scikit-learn libraries.The dataset was loaded as test samples via CSV files, and the LeNet 20 CNN model was employed for classification.The proposed LeNet 20 CNN model achieved significant improvements over existing fetal heart diagnosis models.Key findings include a detection score of 98.32%, F1 score of 98.23%, recall of 97.89%, accuracy of 98.32%, and sensitivity of 97.29%.These results indicate superior detection accuracy and reliability compared to previous methods.results of this study demonstrate notable enhancements over prior fetal heart diagnosis technologies.Specifically, the LeNet 20 CNN model outperformed existing methods in terms of detection accuracy and reliability.This investigation successfully addresses the limitations of conventional fetal heart diagnosis methods by employing CNN deep learning technology.The LeNet 20 architecture serves as an effective classifier and feature extractor, enabling accurate detection of fetal heart anomalies in prior stage.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".