Kernel Piecewise Regressive Gradient Multilayer Perceptron Learning for Early Detection of Breast Cancer Using Mammographic Images
Kernel Piecewise Regressive Gradient Multilayer Perceptron Learning for Early Detection of Breast Cancer Using Mammographic Images |
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| © 2025 by IJETT Journal | ||
| Volume-73 Issue-10 |
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| Year of Publication : 2025 | ||
| Author : Razul Beevi.I, Balaji.T | ||
| DOI : 10.14445/22315381/IJETT-V73I10P109 | ||
How to Cite?
Razul Beevi.I, Balaji.T,"Kernel Piecewise Regressive Gradient Multilayer Perceptron Learning for Early Detection of Breast Cancer Using Mammographic Images", International Journal of Engineering Trends and Technology, vol. 73, no. 10, pp.117-131, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I10P109
Abstract
Breast cancer is considered one of the most life-threatening forms of cancer among women worldwide. Early detection plays a pivotal role in helping doctors diagnose benign from malignant breast cancers for successful treatment and improved outcomes. Conventionally, breast cancer detection using machine learning and deep learning techniques has been developed for early diagnosis and treatment. However, achieving accurate detection with minimal time consumption poses significant challenges. A novel technique named Kernel Piecewise Regressive Gradient Multilayer Perceptron Learning (KPRGMPL) has been developed to address this Issue. It consists of three processes: preprocessing, feature extraction, and classification. The input layer receives numerous mammogram images. These images are processed through hidden layers. Image preprocessing in the first hidden layer is performed using Dixon’s statistical Savitzky-Golay filtering technique by reducing noise artifacts. Radial kernel proximity lambda-connectedness image segmentation is performed in the second hidden layer to segment the image into multiple regions and extract the Region of Interest (ROI). Subsequently, features such as texture and size are extracted from the ROI for accurate cancer detection with minimal time. Finally, classification is carried out in the third hidden layer to detect breast cancer at an earlier stage by employing the Hamann indexive Piecewise Linear Regression(PLR). To minimize the error, a stochastic gradient function is applied. The accurately classified results are then obtained at the output layer. Experiments are evaluated with different evaluation metrics. The observed result shows the effectiveness of the proposed KPRGMPL technique, which has higher accuracy and minimum time than the existing methods.
Keywords
Breast Cancer Detection, Mammographic Images, Kernel Piecewise Regressive Gradient Multilayer Perceptron Learning, Dixon’s Statistical Savitzky-Golay Filtering Technique, Radial Kernel Proximity Lambda-Connectedness Image Segmentation, Hamann Indexive Piecewise Linear Regression.
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