Deep Learning and Fuzzy Logic Based Intelligent Technique for the Image Enhancement and Edge Detection Framework
Bibliographic record
Abstract
Medical imaging is the promising area in digital image processing.Medical images are useful for all types of medical treatment and diagnostics.Medical images are captured through the medical devices, consists some kind of noises and it requires efficient enhancement techniques.Medical imaging also useful in the image segmentation and object detection purposes.Various researcher proposed several types of enhancement techniques and edge detection techniques, but still accuracy and noise are challenge for the enhanced image.So, it is the need of some intelligent techniques to address these issues.In this work we proposed deep learning-based convolution neural network for the image denoising and image enhancement and for the edge detection fuzzy logic-based approach used.The model of DnCNN used here for the image denoising and image enhancement, this model comprises several convolution layers along with input and output layer, this model learns according to the weights and bias.Also, fuzzy logic technique implemented fuzzy inference rules which can give more accurate edges of the image.The result obtained through this hybrid approach is very interesting and effective as compare with previous approaches like histogram-based approach and linear filtering approach.Proposed methods give the promising results as compare with existing methods.All types of simulation performed in MATLAB 2020.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".