A Novel Convolutional Neural Networks-Fused Shallow Classifier for Breast Cancer Detection Sharifa Khalid Alduraibi * Department of Radiology, College of Medicine, Qassim University, Buraidah, 52571, Saudi Arabia *Corresponding Author: Sharifa Khalid Alduraibi. Email: sharifadurraibbi@gmail.com Received: 08 November 2021; Accepted: 13 December 2021 Abstract: This paper proposes a fused methodology based upon convolutional neural networks and a shallow classifier to diagnose and differentiate breast can- cer between malignant lesions and benign lesions. First, various pre-trained con- volutional neural networks are used to calculate the features of breast ultrasonography (BU) images. Then, the computed features are used to train the different shallow classifiers like the tree, naïve Bayes, support vector machine (SVM), k-nearest neighbors, ensemble, and neural network. After extensive train- ing and testing, the DenseNet-201, MobileNet-v2, and ResNet-101 trained SVM show high accuracy. Furthermore, the best BU features are merged to increase the classification accuracy at the cost of high computational time. Finally, the feature dimension reduction ReliefF algorithm is applied to address the computational complexity issue. An online publicly available dataset of 780 BU images is uti- lized to validate the proposed approach. The dataset was further divided into 80 and 20 percent ratios for training and testing the models. After extensive test- ing and comprehensive analysis, it is found that the DenseNet-201 and Mobile- Net-v2 trained SVM has an accuracy of 90.39% and 94.57% for the original and augmented BU images online dataset, respectively. This study concluded that the proposed framework is efficient and can easily be implemented to help and reduce the workload of radiologists/doctors to diagnose breast cancer in female patients. Keywords: Artificial intelligence; machine learning; soft computing; breast cancer detection; classification 1 Introduction Cancer is a term related to a group of disordered dysregulated cells growth, leading to the tumor ’ s development [1]. Among women, one of the most frequently diagnosed cancers is breast cancer worldwide. According to World Health Organization (WHO), nearly 10 million deaths were reported in 2020 due to cancer and is the second leading cause of death [2]. Thus, women’ s breast cancer is one of the leading causes of death globally (685,000 deaths in 2020). In 2020, 2.26 million new cases of breast cancer were reported, which were highest compared to other causes of cancer [2]. Therefore, early detection and differentiation between benign and malignant breast cancer lesions are important for patient 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. Intelligent Automation & Soft Computing DOI:10.32604/iasc.2022.025021 Article ech T Press Science