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Micro Calcification Detection in Mammogram Images Using Contiguous Convolutional Neural Network Algorithm

P. Gomathi1,*, C. Muniraj2, P. S. Periasamy3

1 Selvam College of Technology, Namakkal, 637003, Tamil Nadu, India
2 Knowledge Institute of Technology, Kakapalayam, 637504, Tamil Nadu, India
3 KSR College of Engineering, Tiruchengode, 637215, Namakkal, India

* Corresponding Author: P. Gomathi. Email: email

Computer Systems Science and Engineering 2023, 45(2), 1887-1899. https://doi.org/10.32604/csse.2023.028808

Abstract

The mortality rate decreases as the early detection of Breast Cancer (BC) methods are emerging very fast, and when the starting stage of BC is detected, it is curable. The early detection of the disease depends on the image processing techniques, and it is used to identify the disease easily and accurately, especially the micro calcifications are visible on mammography when they are 0.1 mm or bigger, and cancer cells are about 0.03 mm, which is crucial for identifying in the BC area. To achieve this micro calcification in the BC images, it is necessary to focus on the four main steps presented in this work. There are three significant stages of the process assigned to find the BC using a thermal image; the image processing procedures are described below. In the first stage of the process, the Gaussian filter technique is implemented to magnify the screening image. During the second stage, BC detection is separated from the pre-processed image. The Proposed Versatile K-means clustering (VKC) algorithm with segmentation is used to identify the BC detection form of the screening image. The centroids are then recalculated using proposed VKC, which takes the mean of all data points allocated to that centroid’s cluster, lowering the overall intra-cluster variance in comparison to the prior phase. The “means” in K-means refers to the process of averaging the data and determining a new centroid. This process eliminates unnecessary areas of interest. First, the mammogram screening image information is taken from the patient and begins with the Contiguous Convolutional Neural Network (CCNN) method. The proposed CCNN is used to classify the Micro calcification in the BC spot using the feature values is the fourth stage of the process. The assess the presence of high-definition digital infrared thermography technology and knowledge base and suggests that future diagnostic and treatment services in breast cancer imaging will be developed. The use of sophisticated CCNN techniques in thermography is being developed to attain a greater level of consistency. The implemented (CCNN) technique’s performance is examined with different classification parameters like Recall, Precision, F-measure and accuracy. Finally, the Breast Cancer stages will be classified based on the true positive and true negative values.

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APA Style
Gomathi, P., Muniraj, C., Periasamy, P.S. (2023). Micro calcification detection in mammogram images using contiguous convolutional neural network algorithm. Computer Systems Science and Engineering, 45(2), 1887-1899. https://doi.org/10.32604/csse.2023.028808
Vancouver Style
Gomathi P, Muniraj C, Periasamy PS. Micro calcification detection in mammogram images using contiguous convolutional neural network algorithm. Comput Syst Sci Eng. 2023;45(2):1887-1899 https://doi.org/10.32604/csse.2023.028808
IEEE Style
P. Gomathi, C. Muniraj, and P.S. Periasamy "Micro Calcification Detection in Mammogram Images Using Contiguous Convolutional Neural Network Algorithm," Comput. Syst. Sci. Eng., vol. 45, no. 2, pp. 1887-1899. 2023. https://doi.org/10.32604/csse.2023.028808



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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