International Journal of Electrical and Computer Engineering (IJECE) Vol. 14, No. 1, February 2024, pp. 294~304 ISSN: 2088-8708, DOI: 10.11591/ijece.v14i1.pp294-304  294 Journal homepage: http://ijece.iaescore.com Modified fuzzy rough set technique with stacked autoencoder model for magnetic resonance imaging based breast cancer detection Sachin Kumar Mamdy 1 , Vishwanath Petli 2 1 Department of Electronics and Communication Engineering, VTU RRC, Visvesvaraya Technological University, Belagavi, India 2 Department of Electronics and Communication Engineering, SLN College of Engineering, Raichur, India Article Info ABSTRACT Article history: Received Jul 9, 2023 Revised Jul 8, 2023 Accepted Jul 17, 2023 Breast cancer is the common cancer in women, where early detection reduces the mortality rate. The magnetic resonance imaging (MRI) images are efficient in analyzing breast cancer, but it is hard to identify the abnormalities. The manual breast cancer detection in MRI images is inefficient; therefore, a deep learning-based system is implemented in this manuscript. Initially, the visual quality improvement is done using region growing and adaptive histogram equalization (AHE), and then, the breast lesion is segmented by Otsu thresholding with morphological transform. Next, the features are extracted from the segmented lesion, and a modified fuzzy rough set technique is proposed to reduce the dimensions of the extracted features that decreases the system complexity and computational time. The active features are fed to the stacked autoencoder for classifying the benign and malignant classes. The results demonstrated that the proposed model attained 99% and 99.22% of classification accuracy on the benchmark datasets, which are higher related to the comparative classifiers: decision tree, naïve Bayes, random forest and k-nearest neighbor (KNN). The obtained results state that the proposed model superiorly screens and detects the breast lesions that assists clinicians in effective therapeutic intervention and timely treatment. Keywords: Breast cancer detection Fuzzy rough set Image enhancement Magnetic resonance imaging Otsu thresholding Stacked autoencoder This is an open access article under the CC BY-SA license. Corresponding Author: Sachin Kumar Mamdy Department of Electronics and Communication Engineering, VTU RRC, Visvesvaraya Technological University Belagavi, India Email: msachin834@gmail.com 1. INTRODUCTION In the current scenario, breast cancer is the common cancer type in the rural and urban areas, where women between the age group of thirty-fifty years are at a higher risk of breast cancer [1], [2]. It is the second most cause of cancer deaths in women after lung cancer [3]. Hence, the death rate of women due to breast cancer is 1 in 37 subjects, which is around 2.7%. Therefore, the proper treatment and early diagnosis of breast cancer are essential for decreasing the death rates and preventing the disease progression [4]–[6]. In recent decades, magnetic resonance imaging (MRI) images are highly utilized for diagnosing breast cancer to decrease unnecessary biopsies [7], [8]. Additionally, the MRI images are a highly recommended test to monitor and detect the breast cancer lesion and to interpret the lesioned region, because it has better soft tissue imaging [9]. Additionally, an experienced physician is needed to process the MRI images, which is a time-consuming mechanism [10], [11]. For overcoming the above-stated issue, several automated models are