Breast Thermograms Asymmetry Analysis using Gabor filters

Women are far more likely than males to acquire breast cancer, and current research indicates that this is entirely avoidable. It is also to blame for higher death rates among younger women compared to older women in nearly all developing nations. Medical imaging modalities are continuously in need of development. A variety of medical techniques have been employed to detect breast cancer in women. The most recent studies support mammography for breast cancer screening, although its sensitivity and specificity remain suboptimal, particularly in individuals with thick breast tissue, such as young women. As a result, alternative modalities, such as thermography, are required. Digital Infrared Thermal Imaging (DITI), as it is known, detects and records temperature changes on the skin’s surface. Thermography is well-known for its non-invasive, painless, cost-effective, and high recovery rates, as well as its potential to identify breast cancer at an early stage. Gabor filters are used to extract the textural characteristics of the left and right breasts. Using a support vector machine, the thermograms are then classified as normal or malignant based on textural asymmetry between the breasts (SVM). The accuracy achieved by combining Gabor features with an SVM classifier is around 84.5 percent. The early diagnosis of cancer with thermography enhances the patient’s chances of survival significantly since it may detect the disease in its early stages.

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Using Support Vector Machine Detection of Breast Cancer in Early stage

International Journal for Research in Engineering Application & Management ◽

10.35291/2454-9150.2020.0465 ◽

2020 ◽

pp. 213-216

Keyword(s):

Breast Cancer ◽

Machine Learning ◽

Support Vector Machine ◽

Early Stage ◽

Breast Cancer Diagnosis ◽

Support Vector ◽

Svm Classifier ◽

K Nearest Neighbors ◽

Data Set ◽

Sensitivity Specificity

The Breast Cancer is disease which tremendously increased in women’s nowadays. Mammography is technique of low-powered X-ray diagnosis approach for detection and diagnosis of cancer diseases at early stage. The proposed system shows the solution of two problems. First shows to detect tumors as suspicious regions with a weak contrast to their background and second shows way to extract features which categorize tumors. Hence this classification can be done with SVM, a great method of statistical learning has made significant achievement in various field. Discovered in the early 90’s, which led to an interest in machine learning? Here the different types of tumor like Benign, Malignant, or Normal image are classified using the SVM classifier. This techniques shows how easily we can detect region of tumor is present in mammogram images with more than 80% of accuracy rates for linear classification using SVM. The 10-fold cross validation to get an accurate outcome is been used by proposed system. The Wisconsin breast cancer diagnosis data set is referred from UCI machine learning repository. The considering accuracy, sensitivity, specificity, false discovery rate, false omission rate and Matthews’s correlation coefficient is appraised in the proposed system. This Provides good result for both training and testing phase. The techniques also shows accuracy of 98.57% and 97.14% by use of Support Vector Machine and K-Nearest Neighbors

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Early Detection of Ductal Breast Cancer: The Diagnostic Procedure for Pathological Discharge from the Nipple

Tumori Journal ◽

10.1177/030089167906500506 ◽

1979 ◽

Vol 65 (5) ◽

pp. 555-562 ◽

Cited By ~ 11

Author(s):

Günther Kindermann ◽

Eberhard Paterok ◽

Julius Weishaar ◽

Herwig Egger ◽

Wulf Rummel ◽

...

Keyword(s):

Breast Cancer ◽

Early Stage ◽

Definitive Treatment ◽

Surgical Specimen ◽

Early Diagnosis Of Cancer ◽

Intraductal Cancer ◽

Nonpalpable Lesions ◽

Diagnosis Of Cancer ◽

Work Up

Pathologic discharge from the nipple may be the only symptom of an early stage of carcinoma. Galactography is then the diagnostic method of choice to locate intraductal, nonpalpable lesions. The technique of galactography, the adequate surgical approach of pathologic galactographs (milk-duct segment resection), and the appropriate histological work-up of the surgical specimen are demonstrated. We report on 1918 galactographies in 1363 women with pathological discharge. In only 427 cases was a milk duct segment resection necessary (31.4%). In 8.5%, we found invasive intraductal cancer and in 2.9% ductal carcinomata in situ. Only 1 patient with breast cancer had axillary metastases. Extensive intraductal solid, papillary or adenomatous proliferations were found in 11.9% of the patients with excision. In 46.7% of the patients, papillomas were excised, a definitive treatment for this process. The supposition for success in the early diagnosis of cancer is close teamwork among the radiology, surgery and pathology services: the diagnostic result depends upon this. We attribute our yield of exact diagnosis to a very sophisticated histological work-up. We believe that this is necessary to avoid diagnostic failures.

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SVM Kernel and Genetic Feature Selection Based Automated Diagnosis of Breast Cancer

Recent Advances in Computer Science and Communications ◽

10.2174/2666255813999200818204842 ◽

2020 ◽

Vol 13 ◽

Author(s):

Indu Singh ◽

Shashank Garg ◽

Shivam Arora ◽

Nikhil Arora ◽

Kripali Agrawal

Keyword(s):

Breast Cancer ◽

Genetic Algorithm ◽

Feature Selection ◽

Early Stage ◽

Feature Space ◽

Breast Cancer Diagnosis ◽

Support Vector ◽

Svm Classifier ◽

Human Beings ◽

Novel Approach

Background: Breast cancer is the development of a malignant tumor in the breast of human beings (especially females). If not detected at the initial stages, it can substantially lead to an inoperable construct. It is a reason for majority of cancer-related deaths throughout the world. Objectives: The main aim of our study is to diagnose the breast cancer at early stage so that required treatment can be provided for survival. The tumor is classified as malignant or benign accurately at early stage using a novel approach that includes an ensemble of Genetic Algorithm for feature selection and kernel selection for SVM-Classifier. Methods: The proposed GA-SVM (Genetic Algorithm – Support Vector Machine) algorithm in this paper optimally selects the most appropriate features for training with the SVM classifier. Genetic Programming is used to select the features and the kernel for the SVM classifier. Genetic Algorithm operates by exploring the optimal layout of features for breast cancer, thus, subjugating the problems faced in exponentially immense feature space. Results: The proposed approach accounts for a mean accuracy of 98.82% by using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset available on UCI with the training and testing ratio being 50:50 respectively. Conclusion: The results prove that our proposed model outperforms the previously designed models for breast cancer diagnosis. The outcome assures that the GA-SVM model may be used as an effective tool in assisting the doctors for treating the patients. Alternatively, it may be utilized as an alternate opinion in their eventual diagnosis.

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Analysis of Melanoma Lesion Images using Feature Extraction & Classification Algorithms

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.f8612.038620 ◽

2020 ◽

Vol 8 (6) ◽

pp. 2613-2618

Keyword(s):

Support Vector Machine Classifier ◽

Early Stage ◽

Local Binary Patterns ◽

Polynomial Kernel ◽

Support Vector ◽

Svm Classifier ◽

Phase Identification ◽

Human Beings ◽

Attribute Extraction ◽

Melanoma Skin

Among the most dangerous of cancers found in human beings, skin cancer is the prevalent one. These are of various forms. The most sporadic among them is melanoma. Early phase identification of melanoma will be helpful in curing it. Intensive skin exposure to UV radiation is the principal cause of melanoma. In this article, along with other techniques for extracting features (LDP [Local Directional Patterns], LBP [Local Binary Patterns], Convolutional Neural Networks [CNN]), we have used an SVM classifier for the study of melanoma skin photos. Such suggested algorithms are best graded when opposed to other recognition schemes. The LBP and LDP gives us means to extract features; these figures are subsequently used for identification of derived features from these methods or algorithms and classified or separated by the SVM (Support Vector Machine) classifier. For many of the classifications of melanoma skin images using these algorithms, we have accuracy nearly above 80 %, whereby the LBP system together with the SVM classifier was the most powerful attribute extraction tool of the three with their polynomial kernel type. Thus using this algorithm-classifier, the melanoma skin lesion images can be detected and diagnosed by the doctors in its early stage itself, resultantly, helping save lives.

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Early Discontinuation and Nonadherence to Adjuvant Hormonal Therapy in a Cohort of 8,769 Early-Stage Breast Cancer Patients

Journal of Clinical Oncology ◽

10.1200/jco.2009.25.9655 ◽

2010 ◽

Vol 28 (27) ◽

pp. 4120-4128 ◽

Cited By ~ 438

Author(s):

Dawn L. Hershman ◽

Lawrence H. Kushi ◽

Theresa Shao ◽

Donna Buono ◽

Aaron Kershenbaum ◽

...

Keyword(s):

Breast Cancer ◽

Hormonal Therapy ◽

Early Stage ◽

Optimal Schedule ◽

Medication Possession Ratio ◽

Adjuvant Hormonal Therapy ◽

Breast Cancer Patients ◽

Early Discontinuation ◽

Younger Women ◽

Hormone Sensitive

Purpose While studies have found that adjuvant hormonal therapy for hormone-sensitive breast cancer (BC) dramatically reduces recurrence and mortality, adherence to medications is suboptimal. We investigated the rates and predictors of early discontinuation and nonadherence to hormonal therapy in patients enrolled in Kaiser Permanente of Northern California health system. Patients and Methods We identified women diagnosed with hormone-sensitive stage I-III BC from 1996 to 2007 and used automated pharmacy records to identify hormonal therapy prescriptions and dates of refill. We used Cox proportional hazards regression models to analyze factors associated with early discontinuation and nonadherence (medication possession ratio < 80%) of hormonal therapy. Results We identified 8,769 patients with BC who met our eligibility criteria and who filled at least one prescription for tamoxifen (43%), aromatase inhibitors (26%), or both (30%) within 1 year of diagnosis. Younger or older age, lumpectomy (v mastectomy), and comorbidities were associated with earlier discontinuation, while Asian race, being married, earlier year at diagnosis, receipt of chemotherapy or radiotherapy, and longer prescription refill interval were associated with completion of 4.5 years of therapy. Of those who continued therapy, similar factors were associated with full adherence. Women age younger than 40 years had the highest risk of discontinuation (hazard ratio, 1.51; 95% CI, 1.23 to 1.85). By 4.5 years, 32% discontinued therapy, and of those who continued, 72% were fully adherent. Conclusion Only 49% of patients with BC took adjuvant hormonal therapy for the full duration at the optimal schedule. Younger women are at high risk of nonadherence. Interventions to improve adherence and continuation of hormonal therapy are needed, especially for younger women.

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Is BRCA Mutation Testing Cost Effective for Early Stage Breast Cancer Patients Compared to Routine Clinical Surveillance? The Case of an Upper Middle-Income Country in Asia

Applied Health Economics and Health Policy ◽

10.1007/s40258-018-0384-8 ◽

2018 ◽

Vol 16 (3) ◽

pp. 395-406 ◽

Cited By ~ 3

Author(s):

Ka Keat Lim ◽

Sook Yee Yoon ◽

Nur Aishah Mohd Taib ◽

Fatiha Hana Shabaruddin ◽

Maznah Dahlui ◽

...

Keyword(s):

Breast Cancer ◽

Cancer Patients ◽

Early Stage ◽

Brca Mutation ◽

Cost Effective ◽

Middle Income Country ◽

Breast Cancer Patients ◽

Middle Income ◽

Clinical Surveillance ◽

Testing Cost

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Breast cancer detection using deep convolutional neural networks and support vector machines

PeerJ ◽

10.7717/peerj.6201 ◽

2019 ◽

Vol 7 ◽

pp. e6201 ◽

Cited By ~ 48

Author(s):

Dina A. Ragab ◽

Maha Sharkas ◽

Stephen Marshall ◽

Jinchang Ren

Keyword(s):

Breast Cancer ◽

Breast Imaging ◽

Input Data ◽

Data Augmentation ◽

Area Under The Curve ◽

Screening Mammography ◽

Support Vector ◽

Svm Classifier ◽

Deep Convolutional Neural Networks ◽

Cad System

It is important to detect breast cancer as early as possible. In this manuscript, a new methodology for classifying breast cancer using deep learning and some segmentation techniques are introduced. A new computer aided detection (CAD) system is proposed for classifying benign and malignant mass tumors in breast mammography images. In this CAD system, two segmentation approaches are used. The first approach involves determining the region of interest (ROI) manually, while the second approach uses the technique of threshold and region based. The deep convolutional neural network (DCNN) is used for feature extraction. A well-known DCNN architecture named AlexNet is used and is fine-tuned to classify two classes instead of 1,000 classes. The last fully connected (fc) layer is connected to the support vector machine (SVM) classifier to obtain better accuracy. The results are obtained using the following publicly available datasets (1) the digital database for screening mammography (DDSM); and (2) the Curated Breast Imaging Subset of DDSM (CBIS-DDSM). Training on a large number of data gives high accuracy rate. Nevertheless, the biomedical datasets contain a relatively small number of samples due to limited patient volume. Accordingly, data augmentation is a method for increasing the size of the input data by generating new data from the original input data. There are many forms for the data augmentation; the one used here is the rotation. The accuracy of the new-trained DCNN architecture is 71.01% when cropping the ROI manually from the mammogram. The highest area under the curve (AUC) achieved was 0.88 (88%) for the samples obtained from both segmentation techniques. Moreover, when using the samples obtained from the CBIS-DDSM, the accuracy of the DCNN is increased to 73.6%. Consequently, the SVM accuracy becomes 87.2% with an AUC equaling to 0.94 (94%). This is the highest AUC value compared to previous work using the same conditions.

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