Pixel-Level Feature Extraction Model for Breast Cancer Detection

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