Efficient Feature Selection Using CNN, VGG16 and PCA for Breast Cancer Ultrasound Detection
Bibliographic record
Abstract
Breast cancer frequently leads to fatalities among women worldwide and is the most commonly diagnosed form of cancer in this population.Ultrasound imaging, due to its versatility and non-invasive nature, serves as an auxiliary technique in breast cancer detection.Despite significant improvements in diagnostic methods, the precise and efficient classification of ultrasound images remains a challenge.This study proposes a novel approach to address this issue, employing an integration of deep learning and feature selection techniques aimed at enhancing the accuracy of breast ultrasound image classification.In the presented study, two primary models were proposed for the classification of real-world breast ultrasound images into three distinct categories: normal, benign, and malignant.The first model design leveraged Convolutional Neural Networks (CNN) and VGG16 for feature extraction.Subsequently, the second model incorporated Principal Component Analysis (PCA) into the framework of CNN and VGG16 for feature selection, aiming to reduce dataset dimensionality while preserving the maximum data variance before classification.The dataset used in this study, comprising 1059 breast ultrasound images, was obtained from the Breast Cancer Early Detection Clinic at Imam Al-Sadiq General Teaching Hospital in Babylon, Iraq.Images were categorized into normal, benign, and malignant based upon their respective characteristics.Evaluation of the proposed method was conducted based on accuracy, precision, F1 score, and recall.The classification accuracy for the models was as follows: 93% for CNN, 94% for CNN-PCA, 97% for VGG16, and 96% for VGG16-PCA.The findings of this study have considerable implications for breast cancer detection methodologies.The integration of deep learning techniques and feature selection strategies in our research offers a potentially more efficient and accurate diagnostic framework.Furthermore, this study provides a foundation for future development in ultrasound-based breast cancer detection, and it proposes a blueprint for enhanced diagnostic precision.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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".