## 2 Related Work
For Mini-MIAS dataset it obtained 0.94 and 86.90%, whereas for DDSM it obtained 0.89 and 80.76%, respectively. [PERSON] et al. [37] present an effective technique for mammogram classification by exploiting texture feature-based directional transform. The feature vector is obtained by using the Gabor wavelet transform. Machine learning techniques are used in the decision-making stage. Finally, the performance was tested using the DDSM database and obtained 0.939, 0.951, and 0.92 in terms of accuracy, sensitivity, and specificity, respectively. Haar wavelet decompositions are used to extract texture features by [38]. Points and corners of interest are detected using the speeded-up robust feature (SURF) and minimum eigenvalue algorithm (MinEigenAlg). The subset features of the selected subset using a filter and embedded methods as feature selection techniques are used to train a random forest (RF) binary classifier. This study was tested using 260 images from the BCDR database; it obtained an accuracy of 97.31% and 88.46% for normal/abnormal and benign/malignant, respectively. Eventually, the discrete cosine transform (DCT) is used to extract texture characteristics [28]. Some studies have used statistical and directional transform-based texture feature extraction. Discrete WT (DWT) and GLCM were used for the feature extraction-based fusion method [39; 40]. SURF, Gabor filter, and GLCM were used for feature extraction. ELM was proposed for the identification of microcalcification (MC) [41]. Furthermore, some studies used shape and texture characteristics for mammogram classification. In [42], shape features, FOS, and GLCM of statistical texture features with some intensity-based features are extracted, obtaining 91.42% accuracy for benign or suspicious classification. [PERSON] and [PERSON] [43] extracted shape, texture, and gray level features from mammogram images from the MIAS dataset, and optimal features were selected using the regression tree (CART). With feed-forward NNs, the classification was carried out and an accuracy of 96% was obtained. The varied regions of masses were analyzed using concave geometry (alpha shapes) and geostatistical methods [44]. SVM was applied to evaluate each feature extraction, and the study produced a detection rate of 97.30% and 91.63% in MIAS and DDSM datasets, respectively. Several characteristics, namely intensity, margin, texture, and shape characteristics, were used with SVM to classify breast cancer into benign or malignant categories [45]. Researchers obtained an accuracy of 91.37% and 93.22% in the MIAS and DDSM datasets, respectively. Many studies have exploited the integration between different features of shape and texture to improve the CAD system for the classification of breast cancer [46; 47; 48]. The existing works have some limitations which are used as challenges in this work. Lesion classification in breast cancer detection is considered a key challenge. Mammogram images influenced by noise can lead to the provision of imprecise data, which reduces the performance of the method. Reduced performance is considered an important issue in classification. Mammogram images comprise complex textures, which are a substantial problem in lesion detection. Failure to preserve complete information and obtain satisfactory performance in terms of accuracy for incomplete data is a very serious problem that can result in inappropriate decision-making. Overall, the literature showed that result interpretation is still based on heuristics and various databases with a small set of images for training and testing stages and validation. In addition, performance factors, including feature extraction and classification, have influenced the results of various methods. These limitations indicate that improvements can still be achieved. The literature review showed that many feature extraction techniques have been proposed. However, the fractal dimension method for extraction features has not been sufficiently evaluated and tested to classify mammogram images.