International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 06 | Jun 2025
p-ISSN: 2395-0072
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MAMMOGRAPHIC FEATURE ANALYSIS AND MASS DETECTION IN BREAST CANCER Dr.Karthigai Lakshmi S1, Joicy I2, Senthil Kumar R3 Professor&Head1 ,PG Student2 , Assistant Professor3 Department of Electronics and Communication Engineering, SSM Institute of Engineering and Technology Dindigul,Tamilnadu,India -------------------------------------------------------------------------***----------------------------------------------------------------------While no proven methods exist to prevent breast cancer, ABSTRACT-This research focuses on the automated
early detection remains essential for reducing mortality rates. As a result, substantial efforts have been directed toward early-stage diagnosis. Imaging technologies play a key role in this endeavor [2], with mammography being widely recognized as a standard tool for both screening and diagnosis. It facilitates the detection of breast cancer by identifying anomalies such as masses or micro calcifications
analysis of mammographic features and mass detection for the early diagnosis of breast cancer. It aims to design a robust system that can accurately identify and classify abnormalities in mammographic images using sophisticated image processing techniques such as noise filtering, contrast improvement, and image segmentation to enhance mammogram clarity. The system leverages deep learning methods, particularly Convolutional Neural Networks (CNNs) and Region-based CNNs (R-CNNs), to automatically extract and evaluate critical features like mass shape, texture, and edges, enabling reliable differentiation between benign and malignant tumors. The model is trained on an extensive dataset of annotated mammograms, addressing class imbalance through data augmentation techniques. Its performance is assessed through metrics like accuracy, sensitivity, specificity, and ROC curve analysis to ensure diagnostic effectiveness. The proposed framework aims to minimize reliance on manual readings by radiologists, support early disease detection, and improve diagnostic accuracy—contributing to reduced breast cancer mortality and better healthcare outcomes.
EXISITING MODEL The integration of biometric technologies into Automated Existing models for breast cancer detection using MATLAB typically begin with a preprocessing stage, which is crucial for improving image quality and ensuring accurate analysis in the subsequent steps. In this stage, mammogram or ultrasound images are enhanced using techniques such as histogram equalization, noise removal using filters (like Gaussian or median), and contrast enhancement. This step helps in eliminating artifacts and improving the visibility of important features like masses or microcalcifications, which are indicative of breast cancer. MATLAB provides various built-in functions and toolboxes like the Image Processing Toolbox, which facilitates these operations efficiently.
Keywords:
Mammogram, Convolution Neural Network(CNN), Gray-Level Co-occurrence Matrix (GLCM), Support Vector Machines (SVM), Regions of Interest (ROI), Tumor, Node and Metastasis (TNM).
The next critical module is segmentation, where the preprocessed images are divided into regions of interest (ROI). Segmentation methods such as thresholding, region growing, or advanced techniques like k-means clustering or watershed algorithms are employed to isolate potential tumor regions. Accurate segmentation is essential for differentiating cancerous tissue from healthy tissue. In MATLAB, developers often use region-based segmentation functions and morphological operations to refine the borders and enhance the structure of detected regions, ensuring minimal false positives or negatives.
INTRODUCTION According to the American Cancer Society, breast cancer is the most frequently diagnosed cancer among women [1]. Early detection significantly improves the chances of recovery. Mammography is one of the most effective methods for detecting breast cancer in its early stages [2]. Advances in digital mammography imaging systems have enhanced the ability to identify breast abnormalities, thereby increasing survival rates [3]. Today, computeraided diagnosis (CAD) systems are commonly used to support radiologists in the detection and classification of breast masses. These systems, which are generally wellreceived by radiologists, typically involve stages such as segmentation, feature extraction, and classification [4]. Accurate segmentation of breast masses in mammograms is a crucial step that significantly affects the performance of subsequent processes.
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The final module is feature extraction, where the characteristics of the segmented regions are quantified for further classification. Features such as texture (using GrayLevel Co-occurrence Matrix - GLCM), shape (compactness, roundness), and statistical properties (mean, standard deviation, entropy) are extracted. These features are then fed into machine learning classifiers such as Support Vector Machines (SVM), k-NN, or neural networks for
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