This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. ech T Press Science Computers, Materials & Continua DOI: 10.32604/cmc.2023.031949 Article Pixel-Level Feature Extraction Model for Breast Cancer Detection Nishant Behar* and Manish Shrivastava Guru Ghasidas Vishvavidyalaya, Bilaspur, 45009, India *Corresponding Author: Nishant Behar. Email: nishant.itggv@gmail.com Received: 01 May 2022; Accepted: 27 June 2022 Abstract: 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. Keywords: Breast cancer; machine learning; classification; feature extraction; feature selection 1 Introduction Cancer is caused by cell abnormalities and is a leading cause of death worldwide. The American Cancer Society (ACS) has estimated that 1.9 million new cancer cases were identified and 608,570 people died of the disease in 2021 in the United States alone (1670 deaths/day) [1]. Breast cancer is the most frequently diagnosed form of cancer [2]. Breast cancer detection is an important but difficult task because symptoms of the disease are not prominent in the early stages. A commonly used technique for detecting cancer is the fine-needle aspiration (FNA) procedure, in which tissues are collected from