Mammography Image-Based Diagnosis of Breast Cancer Using Machine Learning: A Pilot Study
| Autor: | Maha M. Alshammari, Afnan Almuhanna, Jamal Alhiyafi |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
K-nearest neighbor
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Breast Neoplasms Pilot Projects TP1-1185 Biochemistry Article Analytical Chemistry breast cancer malignant decision tree Humans support vector machine Electrical and Electronic Engineering Instrumentation machine learning classification Naïve Bayes discriminant analysis benign Chemical technology Bayes Theorem Atomic and Molecular Physics and Optics ComputingMethodologies_PATTERNRECOGNITION Female Algorithms Mammography |
| Zdroj: | Sensors, Vol 22, Iss 203, p 203 (2022) Sensors; Volume 22; Issue 1; Pages: 203 Sensors (Basel, Switzerland) |
| ISSN: | 1424-8220 |
| Popis: | A tumor is an abnormal tissue classified as either benign or malignant. A breast tumor is one of the most common tumors in women. Radiologists use mammograms to identify a breast tumor and classify it, which is a time-consuming process and prone to error due to the complexity of the tumor. In this study, we applied machine learning-based techniques to assist the radiologist in reading mammogram images and classifying the tumor in a very reasonable time interval. We extracted several features from the region of interest in the mammogram, which the radiologist manually annotated. These features are incorporated into a classification engine to train and build the proposed structure classification models. We used a dataset that was not previously seen in the model to evaluate the accuracy of the proposed system following the standard model evaluation schemes. Accordingly, this study found that various factors could affect the performance, which we avoided after experimenting all the possible ways. This study finally recommends using the optimized Support Vector Machine or Naïve Bayes, which produced 100% accuracy after integrating the feature selection and hyper-parameter optimization schemes. |
| Databáze: | OpenAIRE |
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