Morphological and Otsu’s Technique Based Mammography Mass Detection and Deep Neural Network Classifier Based Prediction
Bibliographic record
Abstract
Breast cancer is a leading cause of death among women. The death rate is reduced when this disease is detected early with the help of mammography. Deep learning is a method that radiologists use and request to help them make more accurate diagnoses and enhance their outcome predictions. This work presents a novel strategy comprising a pre-processing method and a mix of morphological and multi-thresholding using Otsu's technique based segmentation technique, which was tested on the Mini-MIAS dataset of 322 images. For Speed-Up Robust Features (SURF) selection, the inbuilt feature extraction is done utilizing multiple colour and texture features approaches. At the classification level, a new layer is added that performs 70 percent training and 30 percent testing of the deep neural network. There are two primary steps in the training phase: (1) Develop a model for dividing breast tissue into dense and non-dense categories. (2) Develop a model for classifying breast regions into mass and non-mass. The results show that the accuracy rate of the proposed automated DL approach is higher than that of other state-of-the-art models. The average accuracy (ACC) rates of the three types of cancer, i.e., normal, benign, and malignant cancer, utilizing the suggested method are 91 percent, 94 percent, and 93 percent, respectively, according to experimental results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".