Sensors, Vol. 24, Pages 2312: Advancing Breast Cancer Diagnosis through Breast Mass Images, Machine Learning, and Regression Models

Sensors, Vol. 24, Pages 2312: Advancing Breast Cancer Diagnosis through Breast Mass Images, Machine Learning, and Regression Models Sensors doi: 10.3390/s24072312 Authors: Amira J. Zaylaa Sylva Kourtian Breast cancer results from a disruption of certain cells in breast tissue that undergo uncontrolled growth and cell division. These cells most often accumulate and form a lump called a tumor, which may be benign (non-cancerous) or malignant (cancerous). Malignant tumors can spread quickly throughout the body, forming tumors in other areas, which is called metastasis. Standard screening techniques are insufficient in the case of metastasis; therefore, new and advanced techniques based on artificial intelligence (AI), machine learning, and regression models have been introduced, the primary aim of which is to automatically diagnose breast cancer through the use of advanced techniques, classifiers, and real images. Real fine-needle aspiration (FNA) images were collected from Wisconsin, and four classifiers were used, including three machine learning models and one regression model: the support vector machine (SVM), naive Bayes (NB), k-nearest neighbors (k-NN), and decision tree (DT)-C4.5. According to the accuracy, sensitivity, and specificity results, the SVM algorithm had the best performance; it was the most powerful computational classifier with a 97.13% accuracy and 97.5% specificity. It also had around a 96% sensitivity for the diagnosis of breast cancer, unlike t...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research