Breast cancer diagnosis based on guided Water Strider Algorithm

Breast cancer is one of the main cancers that effect of the women’s health. This cancer is one of the most important health issues in the world and because of that, diagnosis in the beginning and appropriate cure is very effective in the recovery and survival of patients, so image processing as a decision-making tool can assist physicians in the early diagnosis of cancer. Image processing mechanisms are simple and non-invasive methods for identifying cancer cells that accelerate early detection and ultimately increase the chances of cancer patients surviving. In this study, a pipeline methodology is proposed for optimal diagnosis of the breast cancer area in the mammography images. Based on the proposed method, after image preprocessing and filtering for noise reduction, a simple and fast tumors mass segmentation based on Otsu threshold segmentation and mathematical morphology is proposed. Afterward, for simplifying the final diagnosis, a feature extraction based on 22 structural features is utilized. To reduce and pruning the useless features, an optimized feature selection based on a new developed design of Water Strider Algorithm (WSA), called Guided WSA (GWSA). Finally, the features injected to an optimized SVM classifier based on GWSA for optimal cancer diagnosis. Simulations of the suggested method are applied to the DDSM database. A comparison of the results with several latest approaches are performed to indicate the method higher effectiveness.

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Fusion of Deep Learning and Image Processing Techniques for Breast Cancer Diagnosis

Studies in Computational Intelligence - Deep Learning for Cancer Diagnosis ◽

10.1007/978-981-15-6321-8_1 ◽

2020 ◽

pp. 1-25

Author(s):

V. Ajantha Devi ◽

Anand Nayyar

Keyword(s):

Breast Cancer ◽

Image Processing ◽

Deep Learning ◽

Cancer Diagnosis ◽

Breast Cancer Diagnosis ◽

Image Processing Techniques ◽

Processing Techniques

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A fuzzy based high-resolution multi-view deep CNN for breast cancer diagnosis through SVM classifier on visual analysis

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189174 ◽

2020 ◽

Vol 39 (6) ◽

pp. 8573-8586

Author(s):

Sudhakar Sengan ◽

V. Priya ◽

A. Syed Musthafa ◽

Logesh Ravi ◽

Saravanan Palani ◽

...

Keyword(s):

Breast Cancer ◽

Deep Learning ◽

High Resolution ◽

Cancer Diagnosis ◽

Medical Images ◽

Breast Cancer Diagnosis ◽

Treatment Decisions ◽

Diagnosis And Treatment ◽

Automated System ◽

Svm Classifier

Breast cancer should be diagnosed as early as possible. A new approach of the diagnosis using deep learning for breast cancer and the particular process using segmentation strategies presented in this article. Medical imagery is an essential tool used for both diagnosis and treatment in many fields of medical applications. But, it takes specially trained medical specialists to read medical images and make diagnoses or treatment decisions. New practices of interpreting medical images are labour exhaustive, time-wasting, expensive, and prone to error. Using a computer-aided program which can render diagnosis and treatment decisions automatically would be more beneficial. A new computer-based detection method for the classification between compassionate and malignant mass tumours in mammography images of the breast proposed. (a) We planned to determine how to use the challenging definition, which produces severe examples that boost the segmentation of mammograms. (b) Employing well designing multi-instance learning through deep learning, we validated employing inadequately labelled data of breast cancer diagnosis using a mammogram. (c) The study is going through the Deep Lung method incorporating deep multi-dimensional automated identification and classification of the lung nodule. (d) By combining a probabilistic graphic model in deep learning, it authorizes how weakly labelled data can be used to improve the existing breast cancer identification method. This automated system involves manually defining the Region Of Interest (ROI), with the region and threshold values based on the next region. The High-Resolution Multi-View Deep Convolutional Neural Network (HRMP-DCNN) mainly developed for the extraction of function. The findings collected through the subsequent in available public databases like mammography screening information database and DDSM Curated Breast Imaging Subset. Ultimately, we’ll show the VGG that’s thousands of times quicker, and it is more reliable than earlier programmed anatomy segmentation.

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Image processing and analysis in a dual-modality optoacoustic/ultrasonic system for breast cancer diagnosis

10.1117/12.877389 ◽

2011 ◽

Cited By ~ 2

Author(s):

Vyacheslav Nadvoretskiy ◽

Sergey Ermilov ◽

Hans-Peter Brecht ◽

Richard Su ◽

Alexander Oraevsky

Keyword(s):

Breast Cancer ◽

Image Processing ◽

Cancer Diagnosis ◽

Breast Cancer Diagnosis ◽

Dual Modality ◽

Image Processing And Analysis

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Breast Cancer Diagnosis Using Image Processing and Machine Learning for Elastography Images

2019 8th International Conference on Modern Circuits and Systems Technologies (MOCAST) ◽

10.1109/mocast.2019.8741846 ◽

2019 ◽

Cited By ~ 3

Author(s):

Mohamed Adel ◽

Ahmed Kotb ◽

Omar Farag ◽

M. Saeed Darweesh ◽

Hassan Mostafa

Keyword(s):

Breast Cancer ◽

Machine Learning ◽

Image Processing ◽

Cancer Diagnosis ◽

Breast Cancer Diagnosis

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Breast Cancer Diagnosis Using Image Processing and Machine Learning

Advances in Intelligent Systems and Computing - Emerging Technology in Modelling and Graphics ◽

10.1007/978-981-13-7403-6_12 ◽

2019 ◽

pp. 113-127 ◽

Cited By ~ 1

Author(s):

Subham Sadhukhan ◽

Nityasree Upadhyay ◽

Prerana Chakraborty

Keyword(s):

Breast Cancer ◽

Machine Learning ◽

Image Processing ◽

Cancer Diagnosis ◽

Breast Cancer Diagnosis

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CLO19-046: Follow-Up and Diagnosis of Breast Incidentalomas on Abdominal and Chest MRI

Journal of the National Comprehensive Cancer Network ◽

10.6004/jnccn.2018.7166 ◽

2019 ◽

Vol 17 (3.5) ◽

pp. CLO19-046

Author(s):

Samantha Rios ◽

Kelsey Larson

Keyword(s):

Breast Cancer ◽

Cancer Diagnosis ◽

Breast Mri ◽

Breast Cancer Diagnosis ◽

Final Diagnosis ◽

Key Word ◽

Improve Rate ◽

Work Up

With the increasing use of medical imaging, it is important to report and appropriately recommend work up for incidental findings. The aim of the study was to understand how often incidental breast findings are identified on MRI chest/abdomen protocols, how these findings are followed, and the final diagnosis (benign vs malignant) of these lesions. A single institution retrospective review was performed of women who underwent abdominal or chest MRI from January 2007–January 2017 for a non–breast cancer reason with a radiologic report containing the key word “breast.” Incidental breast findings were defined as lesions not known or suspected prior to imaging. For all patients where a breast lesion was identified, the radiologic reports, follow-up imaging and procedures, and final breast pathology were reviewed. Descriptive points were analyzed using counts and percentages versus mean with standard deviation where applicable. After review, 261 patients met inclusion and exclusion criteria with demographics in Table 1. Most patients (92%) had a known or benign breast finding, but 8% (n=21) had a breast finding for which follow-up was recommended. Recommendation for follow-up included ultrasound (n=4), mammogram (n=8), per clinician (n=14), and breast MRI (n=2). Only 7/21 (33.3%) completed recommended follow-up: 86% (6/7) had normal imaging and 14% (1/7) had a new breast cancer diagnosed. Thus, the rate of new breast cancer diagnosis from abnormal abdominal or chest MRI was 4.7%. Recommendation for specific imaging follow-up (56%) (mammogram/ultrasound/MRI) in the original MRI report was 39% more likely to be completed versus “per clinician” (17%) recommendation (P=.15). Incidental breast findings on abdominal and chest MRI are uncommon, but follow-up is important to exclude new breast cancer diagnosis. Specific imaging recommendations (versus “per clinician”) appeared to improve rate of follow-up. Prior studies have looked at incidental breast findings on CT but few have assessed breast incidentalomas on abdominal MRIs, with similar rates of new breast cancer diagnosis documented in our study. Studies assessing breast incidentalomas on chest MRIs are lacking. Going forward, multi-institutional studies may further define the rate of breast cancer diagnosis after breast incidentalomas identified on abdominal/chest MRI. In addition, studies focusing on improving follow-up imaging rates are important for patient safety and quality of care.

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ENHANCED SVM CLASSIFIER FOR BREAST CANCER DIAGNOSIS

International Journal of Engineering Technologies and Management Research ◽

10.29121/ijetmr.v5.i3.2018.178 ◽

2020 ◽

Vol 5 (3) ◽

pp. 67-74 ◽

Cited By ~ 1

Author(s):

M. Kavitha ◽

G. Lavanya ◽

J. Janani ◽

Balaji. J

Keyword(s):

Breast Cancer ◽

Feature Selection ◽

Cancer Diagnosis ◽

Breast Cancer Diagnosis ◽

Classification Performance ◽

Supervised Machine Learning ◽

Basis Set ◽

Support Vector ◽

Svm Classifier ◽

Breast Image

Breast cancer is the leading disease to cause death especially in women. In this paper, a frame work based algorithm for the classification of cancerous/non-cancerous data is developed using application of supervised machine learning. In feature selection, we derive basis set in the kernel space and then we extend the margin based feature selection algorithm. We are trying to explore several feature selection, extraction techniques and combine the optimal feature subsets with various learning classification methods such as KNN, PNN and Support Vector Machine (SVM) classifiers. The best classification performance for breast cancer diagnosis is attained equal to 99.17% between radius and compact features using SVM classifier. And also derive the features of a breast image in the WBCD dataset.

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