Introduction

Breast cancer is a type of cancer that, due to the abnormal growth of cells, causes a lump in the breast tissue. According to the World Health Organization (WHO), this cancer affects 2.1 million women each year, and also causes the highest number of cancer deaths among women [1]. Mammography X-ray imaging is one of the most common methods used by radiologists to diagnose and screen for breast cancer and to determine the presence of lumps and cysts. In mammography images, very fine calcareous particles are usually seen as noisy particles, and the masses have very low light intensity, making it difficult for radiologists and physicians to detect. Given that accurate and timely diagnosis of cancerous masses, as well as its various types, is of particular importance in the health of individuals in society, the difficulty of diagnosing breast cancer masses, which is mainly associated with diagnostic errors, should be addressed by researchers. In this study, we introduce a new and automatic method to diagnose and detect breast cancer masses with high accuracy. For this purpose, after pre-processing and detecting the borders of the cancerous masses from mammography images, a number of features were extracted from the detected masses and in the end, the feature matrix was applied to the Support Vector Machine (SVM) classifier input. 

Materials and Methods

The images used in this study were collected from the Digital Database for Screening Mammography (DDSM) database [17]. First, for pre-processing of raw images, a 3×3 median filter was applied on digital mammography images to remove noise using MATLAB software. Then, the threshold method was used to extract the cancerous masses. Since the mass edge extracted by the threshold method had inward direction, the brightness of the pixels around the edge was expanded towards the center by using the Dilation operator. After detecting the area of breast cancer masses, we extracted 19 structural features from this area by MATLAB software. Finally, using SVM parameter optimization method by Grasshopper Optimization Algorithm (GOA), as well as using 4-fold cross validation method, data were divided into two categories of benign and cancer.

Results

The values of accuracy, sensitivity, and specificity (along with their variances) resulting from the use of data (benign and cancer) classified by the SVM method using three kernel functions of Linear, Radial Basis Function (RBF) and Polynomial were presented in tables. The final results after applying the GOA were also shown in a separate table. The used training data was 85% and 15% of the data were considered as test data. In 4-fold crossvalidation method, the number of programs executed per kernel function was 100 times. The best results of accuracy, sensitivity and specificity indicators (features) for using RBF kernel function in SVM classifier (before process) were obtained 97%, 100% and 96%, respectively. For linear function after optimization of SVM parameters by GOA, it was obtained 100% for all accuracy, sensitivity, and specificity indicators, which shows the high accuracy of the proposed method. The average values of accuracy, sensitivity and specificity indices for all three SVM kernel functions after applying the optimization algorithm were 95.83, 100 and 94.81%, respectively.

Discussion

Some advantages of this study include a large number of features extracted from masses detected from mammography images, the use of GOA to more accurately determine the type of breast tissue cancer mass, and high speed and accuracy of the proposed algorithm. The boundaries of cancerous tumors were extracted with high accuracy, and finally the classification was performed using simple morphological features. To our knowledge, no study has previously used the optimization methods in the final classification stage. So, it can be said that this the first study that use the GOA to optimize the kernel parameters of different SVM classifiers. This can be the advent of new methods in improving various classification processes in a variety of medical diagnoses. After the detection and diagnosis of breast cancer masses that was performed with high accuracy in this study, according to the morphological and simple features of cancer masses, classification operation was performed well and with high accuracy. The results of this study show the higher performance of the proposed method compared to other methods used in previous related studies [6-16]. 

Ethical Considerations

Compliance with ethical guidelines

Images from DDSM database were used in this research, all images are open access in this database.

Funding

This research did not receive any grant from funding agencies in the public, commercial, or non-profit sectors.

Authors' contributions

Conceptualization, research, methodology: Naser Safdarian; Data collection, resources, writing-original draft: Shadi Yousefian Dezfoulinejad; writing - review & editing: Naser Safdarian.

Conflicts of interest

The authors declared no conflict of interests.

Acknowledgements

The authors would like to thank the Young Researchers and Elite Club of Islamic Azad University, Tabriz branch.

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