K-Means Clustering and Classification of Breast Cancer Images Using Histogram of Oriented Gradients Features and Convolutional Neural Network Models: Diagnostic Image Analysis Study
Bibliographic record
Abstract
Background: Breast cancer has proven to be the most common type of cancer among females around the world. However, mortality rates can be reduced if it is diagnosed at the initial stages. Interpretation made by an expert is required by conventional diagnostic tools such as biopsies and mammograms, and this interpretation can be erroneous. Artificial intelligence has increasingly been used to detect and classify breast cancer. Yet, the acquisition of impressive reliability and accuracy continues to be challenging with these automated systems. Objective: This study aimed to develop an innovative hybrid technique for the classification of breast cancer images involving unsupervised analysis by K-means clustering, feature extraction using Histogram of Oriented Gradients (HOG), and classification of images through a convolutional neural network (CNN). Methods: This study used a dataset of 2788 breast cancer images categorized into benign (n=1480) and malignant (n=1308) classes. The proposed hybrid method included three stages: (1) unsupervised clustering using K-means to group visually similar features; (2) feature extraction using Histogram of Oriented Gradients (HOG) to capture texture and shape patterns; and (3) classification using a CNN trained on the extracted features. The model's performance was evaluated using standard metrics such as accuracy, precision, recall, and F1-score. Results: The CNN achieved a classification accuracy of 98%, with precision, recall, and F1-score values of 0.98 for both benign and malignant cases. K-means clustering revealed distinct groups corresponding to benign and malignant tumors, indicating natural separability in the image data. Conclusions: The combination of HOG feature extraction and CNN-based classification demonstrated high performance in breast cancer detection. The model demonstrates potential utility for automated diagnosis, with possible clinical applications to assist radiologists in identifying malignant tumors more efficiently. Future research will explore additional imaging modalities and real-world clinical testing.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".