Classification of Digital Mammogram based on Nearest-Neighbor Method for Breast Cancer Detection

Nusantara, Anggrek Citra; Purwanti, Endah; Soelistiono, Soegianto

doi:10.14716/ijtech.v7i1.1393

Cited by 19 publications

(8 citation statements)

References 4 publications

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“…A secretory carcinoma with a rounded contour can be mistaken for a fibroadenoma (FA) or a papilloma in imaging studies, especially in a young patient. Mammograms exhibit calcifications in rare cases only (2,8,14,15). Sonography discloses a solid, hypoechoic to isoechoic mass, which may have a microlobulated border.…”

Section: Discussionmentioning

confidence: 99%

Secretory Carcinoma of the Breast in a 24-Year-Old Woman: A Case Report

Fadhilaturrahmi¹,

Delyuzar²

2022

Gac Méd Caracas

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Secretory carcinoma of the breast is a rare invasive carcinoma. This carcinoma shows more aggressive symptoms in older patients. In a reported case of a 24-year-old woman with a mass in her right breast, the histopathologic features consisted of tumor cell proliferation and most of it came in tubular and microcystic structures. The cell nuclei enlarged into a round and oval shape. The chromatin was coarse and came with some protuberant nucleus, foamy, and slightly granular eosinophilic cytoplasm.

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Section: Discussionmentioning

confidence: 99%

Secretory Carcinoma of the Breast in a 24-Year-Old Woman: A Case Report

Fadhilaturrahmi¹,

Delyuzar²

2022

Gac Méd Caracas

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“…K nearest neighbors (KNN) classifier is used in literature to diagnose mammograms [154][155][156][157]. is classifier is a type of majority vote and a nonparametric classifier based on a similarity function.…”

Section: Classificationmentioning

confidence: 99%

Methods Used in Computer-Aided Diagnosis for Breast Cancer Detection Using Mammograms: A Review

Ramadan

2020

Journal of Healthcare Engineering

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According to the American Cancer Society’s forecasts for 2019, there will be about 268,600 new cases in the United States with invasive breast cancer in women, about 62,930 new noninvasive cases, and about 41,760 death cases from breast cancer. As a result, there is a high demand for breast imaging specialists as indicated in a recent report for the Institute of Medicine and National Research Council. One way to meet this demand is through developing Computer-Aided Diagnosis (CAD) systems for breast cancer detection and diagnosis using mammograms. This study aims to review recent advancements and developments in CAD systems for breast cancer detection and diagnosis using mammograms and to give an overview of the methods used in its steps starting from preprocessing and enhancement step and ending in classification step. The current level of performance for the CAD systems is encouraging but not enough to make CAD systems standalone detection and diagnose clinical systems. Unless the performance of CAD systems enhanced dramatically from its current level by enhancing the existing methods, exploiting new promising methods in pattern recognition like data augmentation in deep learning and exploiting the advances in computational power of computers, CAD systems will continue to be a second opinion clinical procedure.

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“…where ∈ ℝ × is a sparse dictionary − a matrix used as a domain to represent a signal that has been reconstructed to be sparse, so x can be ascertained sparse. Wavelet transform (WT), Fast Fourier Transform (FFT) and Discrete Cosine Transform (DCT) are used for the processing, projection and decomposition of the signal (Nusantara et al, 2016;Muntasa, 2017;Basari and Kurniawan, 2019). In this research, DCT is selected as the sparse dictionary.…”

Section: Framework Implementation and Evaluationmentioning

confidence: 99%

Design of Microwave-based Brain Tumor Detection Framework with the Development of Sparse and Low-Rank Compressive Sensing Image Reconstruction

Sholeh

Rizkinia²,

Basari³

2020

IJTech

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Cancer is one of the leading causes of death, and the brain is one of the body's cancerprone organs. The early detection of brain tumors can reduce cancer risk, which is practically assisted and conducted using scanners such as computed tomography (CT) and magnetic resonance imaging (MRI). However, those modalities are high-cost and large-sized, and they have a side effect risk to health. Alternatively, microwave imaging offers a novel cancer scanning method for early detection with low cost, small size and low health risk. Consequently, this research designs and creates a framework with a novel microwave image reconstruction algorithm inside. The framework is a component of the controller and image reconstructor for a portable microwavebased brain tumor detector that is open source and multi-platform. For the novel algorithm, this research proposes a CS-based imaging algorithm by exploiting the data's sparse and low-rank properties. The experiment shows that the proposed algorithm can give better qualitative and quantitative reconstruction results compared to a full-sampling-based as well as CS-based algorithm.

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Classification of Digital Mammogram based on Nearest-Neighbor Method for Breast Cancer Detection

Cited by 19 publications

References 4 publications

Secretory Carcinoma of the Breast in a 24-Year-Old Woman: A Case Report

Secretory Carcinoma of the Breast in a 24-Year-Old Woman: A Case Report

Methods Used in Computer-Aided Diagnosis for Breast Cancer Detection Using Mammograms: A Review

Design of Microwave-based Brain Tumor Detection Framework with the Development of Sparse and Low-Rank Compressive Sensing Image Reconstruction

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