_Applied_  
_sciences_  
_Article_  
**Breast Cancer Detection Using Mammogram Images with Improved Multi-Fractal Dimension Approach and Feature Fusion**  
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malignancy that causes deaths in women among all cancers. Mastectomy can be avoided by early detection of cancer [1]. Disease prevention remains a major challenge because the cause of breast cancer is still unknown. However, effectively diagnosing breast cancer at an early stage can increase the possibility of total recovery. Early detection of breast cancer using mammography [2] and other imaging modalities such as ultrasound [3], magnetic resonance imaging (MRI) [4], and thermal images [5] can help reduce the mortality rate and the probability of recurrence due to the early detection of benign and malignant breast cancer masses with the progress of mammogram imaging. However, expert radiologists are still missing a significant proportion of abnormalities in the early stage of cancer. Given many images available for each radiologist, the main problem is the identification of lesions or suspicious regions in X-ray images. In the early detection of breast cancer, mammography is widely used to detect presymptomatic cancer cells, which in turn helps to prevent the advancement of cancer into critical stage through administering suitable treatments [6]. The latest advancements in mammography include breast tomosynthesis, computer-aided detection, and digital mammography [7]. When it comes to radiographic image interpretation, the evaluation and organization of image data are required to arrive at a diagnosis. These procedures can be difficult to perform due to the variability of breast parenchyma and structural noise caused by the masking of dense tissue [7]. These elements can cause cancer lesions to be buried or ignored. Manual diagnostic involves multiple subjective decisions with increased variations between and within observers that can lead to serious errors and health implications [8]. Clinical professionals have strongly argued for intolerance toward 'false alarms' [9]. Therefore, the development of a computer-aided diagnosis (CAD) system as a decision support tool adds value in terms of helping the radiologist to reduce false-positive and false-negative cases. However, the radiologist experience is still the guide and the final decision maker because ground truth for the training stage is reflected in the radiologist labels [10; 11]. Furthermore, manually interpreting medical images in large volumes consumes a significant amount of time, is a monotonous process, and is vulnerable to mistakes and biases due to the nature of human judgments. The workload of radiologists can be reduced with computerized analysis supported by artificial intelligence techniques, which can help detect tumors and malignant tissues [12]. The structure and characteristics of breast abnormalities make the detection of abnormalities challenging. Consequently, CAD systems were developed to identify breast malignancies and assist medical professionals in the efficient interpretation of medical images with increased accuracy and speed [13; 14]. The role of CAD systems is to solve the challenge of interpreting mammogram images for the effective diagnosis of cancer [15; 16]. The confidence level of the final decision for the detection of breast cancer is a significant advantage of CAD systems when expert radiologists use the CAD as a second opinion in the classification of breast cancer, as well as to improve the sensitivity of the diagnosis [17]. CAD systems generally perform image preprocessing, image segmentation, feature extraction, feature selection, and classification.