Abstract
Mammography is a significant screening test for early detection of breast cancer, which increases the patient's chances of complete recovery. In this paper, a clustering method is presented for the detection of breast cancer tumor locations and areas. To implement the clustering method, we used the growth region approach. This method detects similar pixels nearby. To find the best initial point for detection, it is essential to remove human interaction in clustering. Therefore, in this paper, the FCM-GA algorithm is used to find the best point for starting growth. Their results are compared with the manual selection method and Gaussian Mixture Model method for verification. The classification is performed to diagnose breast cancer type in two primary datasets of MIAS and BI-RADS using features of GLCM and probabilistic neural network (PNN). Results of clustering show that the presented FCM-GA method outperforms other methods. Moreover, the accuracy of the clustering method for FCM-GA is 94%, as the best approach used in this paper. Furthermore, the result shows that the PNN methods have high accuracy and sensitivity with the MIAS dataset.
Highlights
Breast cancer is a deadly and frequent illness that affects people all over the world
RetinaNet, the R–convolutional neural network (CNN) mask can classify in breast tissue finite boxes in any range of scales and aspect ratios in any situation by segmenting the pixel surface
To pick the initial points, this study proposes an algorithm that focuses on Fuzzy C-Means (FCM)-Genetic Algorithm (GA) methods. e purpose of the algorithm is to use the fuzzy clustering algorithm to implement clustering initially
Summary
Breast cancer is a deadly and frequent illness that affects people all over the world. In the 20 years, the number of new breast cancer patients is expected to increase by 75 percent. According to the WHO in 2019, precise and early detection plays a critical role in developing the diagnostic and increasing the patients’ survival rate with breast cancer from 20% to 60%. Tumors come in various forms that must be identified independently since each might lead to different treatment options and prognoses [1]. To aid oncologic decision-making, cancer categorization strives to give an accurate diagnosis of the illness and a prognosis of tumor activity. E biology that underpins cancer genesis and progression is complex. Recent high-throughput technology results have added to our understanding of breast cancer’s underlying genetic changes and biological processes [2]
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