Pixel-Level Feature Extraction Model for Breast Cancer Detection
نویسندگان
چکیده
Breast cancer is the most prevalent among women, and diagnosing it early vital for successful treatment. The examination of images captured during biopsies plays an important role in determining whether a patient has or not. However, stochastic patterns, varying intensities colors, large sizes these make challenging to identify mark malignant regions them. Against this backdrop, study proposes approach pixel categorization based on genetic algorithm (GA) principal component analysis (PCA). spatial features were extracted using various filters, ones are selected GA fed into classifiers pixel-level categorization. Three classifiers—random forest (RF), decision tree (DT), extra (ET)—were used proposed model. parameters all models separately tuned, their performance was tested. results show that by GA+PCA model influential reliable classification service image annotation tumor identification. Further, from benign, malignant, normal classes randomly test GA-PCA-DT delivered accuracies between 0.99 1.0 reduced feature set. predicted sets also compared with respective ground-truth values assess overall method two metrics—the universal quality index (UIQI) structural similarity (SSI). Both measures excellent results.
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ژورنال
عنوان ژورنال: Computers, materials & continua
سال: 2023
ISSN: ['1546-2218', '1546-2226']
DOI: https://doi.org/10.32604/cmc.2023.031949