Abstract
Breast cancer diagnosis and classification utilizing a CAD system with mammography pictures as input remains a difficult task in healthcare management systems. Microcalcification (MCs) is a microscopic deposit of calcium particles that acts as an early indicator of breast cancer. Without the assistance of an expert radiologist, automatic recognition of the MC region is a difficult task for a CAD system. MCs appear as bright small spots embedded in normal tissues on mammography images, and classification into normal, benign, and malignant is difficult. Clinical investigations demonstrate that benign zones are much denser than malignant regions, and that the malignant are more clustered than the benign. In this paper, we offer an entropy technique-based framework for autonomously identifying cancer regions. Fractal, topological, and statistical properties are retrieved to classify cancer as benign or malignant. An ensemble classifier comprising a combination of K-Nearest Neighbours (KNN), Support Vector Machine (SVM), Random Forest (RF), and Naive Bayes (NB) classifiers is introduced for successful output prediction. Our suggested model’s performance was evaluated using several metrics such as accuracy, specificity, sensitivity, precision, and recall, and it was found to be superior to existing techniques.
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Vidivelli, S., Sathiya Devi, S. (2023). Microcalcification Detection Using Ensemble Classifier. In: Shukla, A., Murthy, B.K., Hasteer, N., Van Belle, JP. (eds) Computational Intelligence. Lecture Notes in Electrical Engineering, vol 968. Springer, Singapore. https://doi.org/10.1007/978-981-19-7346-8_24
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DOI: https://doi.org/10.1007/978-981-19-7346-8_24
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