An improved k-nearest neighbour method to diagnose breast cancer

An algorithm of entropy weighted local-hyperplane k-nearest-neighbor is proposed for the identification of Raman spectra and is effective for cancer diagnosis.

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Presentation of Novel Architecture for Diagnosis and Identifying Breast Cancer Location Based on Ultrasound Images Using Machine Learning

Diagnostics ◽

10.3390/diagnostics11101870 ◽

2021 ◽

Vol 11 (10) ◽

pp. 1870

Author(s):

Yaghoub Pourasad ◽

Esmaeil Zarouri ◽

Mohammad Salemizadeh Parizi ◽

Amin Salih Mohammed

Keyword(s):

Breast Cancer ◽

High Performance ◽

Nearest Neighbor ◽

Support Vector ◽

Ultrasound Images ◽

Morphological Operations ◽

K Nearest Neighbor ◽

Diagnose Breast Cancer ◽

Premature Deaths ◽

Infected Area

Breast cancer is one of the main causes of death among women worldwide. Early detection of this disease helps reduce the number of premature deaths. This research aims to design a method for identifying and diagnosing breast tumors based on ultrasound images. For this purpose, six techniques have been performed to detect and segment ultrasound images. Features of images are extracted using the fractal method. Moreover, k-nearest neighbor, support vector machine, decision tree, and Naïve Bayes classification techniques are used to classify images. Then, the convolutional neural network (CNN) architecture is designed to classify breast cancer based on ultrasound images directly. The presented model obtains the accuracy of the training set to 99.8%. Regarding the test results, this diagnosis validation is associated with 88.5% sensitivity. Based on the findings of this study, it can be concluded that the proposed high-potential CNN algorithm can be used to diagnose breast cancer from ultrasound images. The second presented CNN model can identify the original location of the tumor. The results show 92% of the images in the high-performance region with an AUC above 0.6. The proposed model can identify the tumor’s location and volume by morphological operations as a post-processing algorithm. These findings can also be used to monitor patients and prevent the growth of the infected area.

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Breast Cancer Diagnosis With Mammography

Biomedical Computing for Breast Cancer Detection and Diagnosis - Advances in Bioinformatics and Biomedical Engineering ◽

10.4018/978-1-7998-3456-4.ch006 ◽

2021 ◽

pp. 107-127

Author(s):

Abir Baâzaoui ◽

Walid Barhoumi

Keyword(s):

Breast Cancer ◽

Cancer Diagnosis ◽

Gold Standard ◽

Breast Cancer Diagnosis ◽

Black Box ◽

Diagnose Breast Cancer ◽

Cad Systems ◽

Helpful Tool ◽

Cancerous Lesion ◽

Aided Diagnosis

Breast cancer, which is the second-most common and leading cause of cancer death among women, has witnessed growing interest in the two last decades. Fortunately, its early detection is the most effective way to detect and diagnose breast cancer. Although mammography is the gold standard for screening, its difficult interpretation leads to an increase in missed cancers and misinterpreted non-cancerous lesion rates. Therefore, computer-aided diagnosis (CAD) systems can be a great helpful tool for assisting radiologists in mammogram interpretation. Nonetheless, these systems are limited by their black-box outputs, which decreases the radiologists' confidence. To circumvent this limit, content-based mammogram retrieval (CBMR) is used as an alternative to traditional CAD systems. Herein, authors systematically review the state-of-the-art on mammography-based breast cancer CAD methods, while focusing on recent advances in CBMR methods. In order to have a complete review, mammography imaging principles and its correlation with breast anatomy are also discussed.

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CytoPAN—Portable cellular analyses for rapid point-of-care cancer diagnosis

Science Translational Medicine ◽

10.1126/scitranslmed.aaz9746 ◽

2020 ◽

Vol 12 (555) ◽

pp. eaaz9746

Author(s):

Jouha Min ◽

Lip Ket Chin ◽

Juhyun Oh ◽

Christian Landeros ◽

Claudio Vinegoni ◽

...

Keyword(s):

Breast Cancer ◽

Cancer Diagnosis ◽

Point Of Care ◽

Growth Factor Receptor ◽

Image Cytometry ◽

Breast Cancer Diagnosis ◽

Needle Aspiration ◽

Current Standard ◽

Diagnose Breast Cancer ◽

Palpable Mass

Rapid, automated, point-of-care cellular diagnosis of cancer remains difficult in remote settings due to lack of specialists and medical infrastructure. To address the need for same-day diagnosis, we developed an automated image cytometry system (CytoPAN) that allows rapid breast cancer diagnosis of scant cellular specimens obtained by fine needle aspiration (FNA) of palpable mass lesions. The system is devoid of moving parts for stable operations, harnesses optimized antibody kits for multiplexed analysis, and offers a user-friendly interface with automated analysis for rapid diagnoses. Through extensive optimization and validation using cell lines and mouse models, we established breast cancer diagnosis and receptor subtyping in 1 hour using as few as 50 harvested cells. In a prospective patient cohort study (n = 68), we showed that the diagnostic accuracy was 100% for cancer detection and the receptor subtyping accuracy was 96% for human epidermal growth factor receptor 2 and 93% for hormonal receptors (ER/PR), two key biomarkers associated with breast cancer. A combination of FNA and CytoPAN offers faster, less invasive cancer diagnoses than the current standard (core biopsy and histopathology). This approach should enable the ability to more rapidly diagnose breast cancer in global and remote settings.

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Evaluation of the Reconfiguration of the Data Acquisition System for 3D USCT

International Journal of Reconfigurable Computing ◽

10.1155/2011/952937 ◽

2011 ◽

Vol 2011 ◽

pp. 1-9 ◽

Cited By ~ 4

Author(s):

Matthias Birk ◽

Clemens Hagner ◽

Matthias Balzer ◽

Nicole V. Ruiter ◽

Michael Hübner ◽

...

Keyword(s):

Breast Cancer ◽

Data Acquisition ◽

Cancer Diagnosis ◽

Three Dimensional ◽

Breast Cancer Diagnosis ◽

Data Acquisition System ◽

Screening Methods ◽

Diagnose Breast Cancer ◽

Bidirectional Communication ◽

Existing Data

As today's standard screening methods often fail to diagnose breast cancer before metastases have developed, an earlier breast cancer diagnosis is still a major challenge. To improve this situation, we are currently developing a fully three-dimensional ultrasound computer tomography (3D USCT) system, promising high-quality volume images of the breast. For obtaining these images, a time-consuming reconstruction has to be performed. As this is currently done on a PC, parallel processing in reconfigurable hardware could accelerate both signal and image processing. In this work, we investigated the suitability of an existing data acquisition (DAQ) system for further computation tasks. The reconfiguration features of the embedded FPGAs have been exploited to enhance the systems functionality. We have adapted the DAQ system to allow for bidirectional communication and to provide an overall process control. Our results show that the studied system can be applied for data processing.

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A genetic algorithm based nearest neighbor classification to breast cancer diagnosis

Australasian Physical & Engineering Sciences in Medicine ◽

10.1007/bf03178690 ◽

2003 ◽

Vol 26 (1) ◽

pp. 6-11 ◽

Cited By ~ 3

Author(s):

R. Jain ◽

J. Mazumdar

Keyword(s):

Breast Cancer ◽

Genetic Algorithm ◽

Cancer Diagnosis ◽

Nearest Neighbor ◽

Breast Cancer Diagnosis ◽

Nearest Neighbor Classification ◽

Neighbor Classification

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Breast Cancer Diagnosis by using k-Nearest Neighbor with Different Distances and Classification Rules

International Journal of Computer Applications ◽

10.5120/10041-4635 ◽

2013 ◽

Vol 62 (1) ◽

pp. 1-5 ◽

Cited By ~ 17

Author(s):

Seyyid AhmedMedjahed ◽

Tamazouzt Ait Saadi ◽

Abdelkader Benyettou

Keyword(s):

Breast Cancer ◽

Cancer Diagnosis ◽

Nearest Neighbor ◽

Breast Cancer Diagnosis ◽

Classification Rules ◽

K Nearest Neighbor

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Increased Mammogram Image Contrast Using Histogram Equalization And Gaussian In The Classification Of Breast Cancer

Journal on Information Technology and Computer Engineering ◽

10.25077/jitce.4.01.40-44.2020 ◽

2020 ◽

Vol 4 (01) ◽

pp. 40-44

Author(s):

Febri Liantoni ◽

Coana Sukmagautama ◽

Risalina Myrtha

Keyword(s):

Breast Cancer ◽

Contrast Enhancement ◽

Nearest Neighbor ◽

Region Of Interest ◽

Histogram Equalization ◽

Image Contrast ◽

K Nearest Neighbor ◽

Diagnose Breast Cancer ◽

Image Contrast Enhancement ◽

Mammogram Image

Breast cancer is one of the most common diseases among women in several countries. One of the most common methods to diagnose breast cancer is mammography. In this study, we propose a classification study to differentiate benign and malignant breast tumors based on mammogram image. The proposed system includes five major steps, i.e. preprocessing, histogram equalization, convolution, feature extraction, and classification. Image is cropped using region of interest (ROI) at preprocessing stage. In this study, we perform image contrast quality enhancement of the mammogram to view the breast cancer better. Image contrast enhancement uses histogram equalization and Gaussian filter. Gray-Level Co-Occurrence Matrix (GLCM) is used to extract the mammogram features. There are five features used i.e. entropy, correlation, contrast, homogeneity, and variance. The last step is to classify using naïve Bayes classifier (NBC) and k-nearest neighbor (KNN). Based on the hypothesis, the accuracy of NBC method is 90% and the accuracy of KKN method is 87.5%. So, the mammogram image contrast enhancement is well performed.

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