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Breast cancer is the most commonly found in women which causes deaths who are aged from 20 to 59. According to the Ministry of Health and Medical Education, it has become the most common disease in recent years in Iran (Ganggayah-Taib, et al., 2019). Today, 88% of women diagnosed with breast cancer have a life expectancy of 10 years. In the United States, it has been reported that about 12% of women were identified during their lifetime, and were referred to as the second cause of women’s death (Houssein-Emam, et al., 2021). Diagnosing the disease at the earlier stages is important because in the early stages, cancer masses are restricted to the breast and the chance of surgical treatment in a less invasive manner is increased. The mortality rate is also decreased in the early stage (Beura-Majhi, et al., 2015; Arulkumar, Lakshmi, and Rao, 2021). Also, the use of classifiers such as artificial neural networks in various fields of engineering sciences is increasing to analyze the time series and various issues of classification. Due to the invention of techniques in the recent era for early diagnosis of breast cancer, the survival rate of the patients is improved. Nowadays, X-ray mammography and MRI (Magnetic Resonant Imaging) techniques are widely utilized with few implications and limitations. X-ray is very harm due to the ionizing radiation and thus its contact with patients has to be only for very short duration. Conversely, MRI technique is expensive while mammography is of less cost, but hard to provide consistency and accuracy in analysing breast cancer. Moreover, errors occur while analysis (Hamian-Darvishan, et al., 2018).
To increase the rate of accuracy and reduce the occurrence of errors, supervised machine learning approaches like KNN, SVM, LSSVM are developed. These models efficiently classify the features as normal or abnormal classes. These methods are complex and even tedious with low CR. Therefore, to provide a solution for all the drawbacks of breast cancer, an optimal classification model is required for which machine learning approaches based on image processing are developed to classify cancer and non-cancer images which involved mammogram images. As the features are essential to discriminate breast cancer as benign or malignant, feature extraction process is of most important. Once the features are extracted, properties of the image like depth, coarseness, smoothness, and regularity are obtained with the help of segmentation process (Leng-Li, et al., 2018). Scientifically, with breast cancer, division of tumor cells is uncontrolled and abnormal tumor cells need more nutrients for growing continuously and to reproduce (Gupta, 2021). Image analysis is the process of producing images on a computer with the goal of determining what things are visible in the image. The process of segmenting a picture into its constituent parts is known as image segmentation. It is one of the most important tasks in autonomous image analysis since the outcomes of segmentation will influence all subsequent tasks, such as feature extraction and object classification. Because of its importance, the segmentation procedure and approach have received a lot of attention in recent decades. This has resulted in a large number of (thousands) of distinct algorithms, and the number is continually growing.