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6 articles for “Digital Mammograms”
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Research ArticleOpen accessPhrase in the titleMass Classification in Digital Mammograms based on Discrete Shearlet TransformJ. Amjath Ali, J. JanetThe most significant health problem in the world is breast cancer and early detection is the key to predict it. Mammography is the most reliable method to diagnose breast cancer at the earliest. The classification of the two most findings in the digital mammograms, micro calcifications and mass are valuable for early detection. Since, the appearance of the masses are … …Journal of Computer Science, Vol. 9, Issue 6 · 1 Jun 2013 · 6,549 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleA robust Approach to Classify Microcalcification in Digital Mammograms Using Contourlet Transform and Support Vector MachineJ. S. Leena Jasmine, S. Baskaran, A. GovardhanMammogram is the best available radiographic method to detect breast cancer in the early stage. However detecting a microcalcification clusters in the early stage is a tough task for the radiologist. Herein we present a novel approach for classifying microcalcification in digital mammograms using Nonsubsampled Contourlet Transform (NSCT) and Support Vector Machine (SVM). The classification of microcalcification is achieved by … …American Journal of Engineering and Applied Sciences, Vol. 6, Issue 1 · 28 Feb 2013 · 7,856 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleWavelet Analysis and Orthogonal Moments based Classification of Microcalcification in Digital MammogramsN. V.S. Sree Rathna Lakshmi, C. ManoharanProblem statement: An important early sign of breast cancer is the clusters of micro calcifications in digital mammograms. To assist radiologists in the diagnosis of mammographic clusters a novel hybrid algorithm has been developed. Approach: A method, for detecting micro calcification in mammograms based on combined feature set with Ant Colony Optimization (ACO) was proposed. The diagonal matrix ‘S’ obtained … …Journal of Computer Science, Vol. 7, Issue 10 · 6 Aug 2011 · 6,558 accessesView PDFCite
Research ArticleOpen accessIn the keywordsGAUSSIAN MIXTURE MODEL BASED CLASSIFICATION OF MICROCALCIFICATION IN MAMMOGRAMS USING DYADIC WAVELET TRANSFORMSuman Mishra, Hariharan RanganathanBreast cancer is a serious health related issue for women in the world. Cancer detected at premature stages has a higher probability of being cured, whereas at advanced stages chances of survival are bleak. Screening programs aid in detecting potential breast cancer at early stages of the disease. Among the various screening programs, mammography is the proven standard for screening … …Journal of Computer Science, Vol. 9, Issue 10 · 5 Sep 2013 · 6,867 accessesView PDFCite
Research ArticleOpen accessIn the abstractImage Pre-Processing Techniques to Improve DBT Classification Accuracy With CNN ResNet-50Qodri Aziz, Slamet Riyadi et al.Digital Breast Tomosynthesis (DBT) is a three-dimensional imaging technology that is meant to determine breast cancer more accurately than regular mammograms. But low contrast and noise can make DBT images less clear, which can make deep learning-based classification less accurate. This study seeks to assess the impact of diverse preprocessing techniques on breast cancer classification outcomes utilizing the ResNet-50 Convolutional … …Journal of Computer Science, Vol. 22, Issue 10 · 21 Oct 2026 · 349 accessesView PDFCite
Research ArticleOpen accessIn the abstractMammogram Analysis Based on Pixel Intensity Mean FeaturesR. Nithya, B. SanthiProblem statement: In the recent years, Computer Aided Diagnosis (CAD) can be very useful for detection of breast cancer. Mammography can be used as an efficient tool for breast cancer diagnosis. A computer based diagnosis and classification system can reduce unnecessary biopsy. Approach: This study investigates a new approach to the classification of mammogram images based on pixel intensity mean … …Journal of Computer Science, Vol. 8, Issue 3 · 7 Jan 2012 · 7,666 accessesView PDFCite