Mammography classification using modified hybrid SVM-KNN

Sonar, Poonam; Bhosle, Udhav; Choudhury, Chandrajit

doi:10.1109/cspc.2017.8305858

Cited by 22 publications

(9 citation statements)

References 10 publications

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“…In [17], the authors extracted GLCM features from the MIAS dataset. These features are passed to a hybrid classifier called KNN with SVM for classification.…”

Section: Related Workmentioning

confidence: 99%

A Robust Feature Extraction Technique for Breast Cancer Detection using Digital Mammograms based on Advanced GLCM Approach

Kumari¹,

Jagadesh²

2022

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: Breast cancer is the most hazardous disease among women worldwide. A simple, cost-effective, and efficient screening called mammographic imaging is used to find the breast abnormalities to detect breast cancer in the early stages so that the patient's health can be improved. OBJECTIVES: The main challenge is to extract the features by using a novel technique called Advanced Gray-Level Cooccurrence Matrix (AGLCM) from pre-processed images and to classify the images using machine learning algorithms. METHODS: To achieve this, we proposed a four-step process: image acquisition, pre-processing, feature extraction, and classification. Initially, a pre-processing technique called Contrast Limited Advanced Histogram Equalization (CLAHE) is used to increase the contrast of images and the features are retrieved using AGLCM which extracts texture, intensity and shape-based features as these are important to identify the abnormality. RESULTS: In our framework, a classifier called eXtreme Gradient Boosting (XGBoost) is applied on mammograms and the results are compared with other classifiers such as Random Forest (RF), K-Nearest Neighbor (KNN), Artificial Neural Networks (ANN), and Support Vector Machine (SVM). The experiments are done on the Mammographic Image Analysis Society (MIAS) dataset. CONCLUSION: The outcome achieved with CLAHE+ AGLCM+ XGBoost classifier is better than the existing methods.In future, we experiment on large datasets and also concentrate on optimal features selection to increase the classification.

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“…In [17], the authors extracted GLCM features from the MIAS dataset. These features are passed to a hybrid classifier called KNN with SVM for classification.…”

Section: Related Workmentioning

confidence: 99%

A Robust Feature Extraction Technique for Breast Cancer Detection using Digital Mammograms based on Advanced GLCM Approach

Kumari¹,

Jagadesh²

2022

EAI Endorsed Trans Perv Health Tech

View full text Add to dashboard Cite

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“…Feature Selection and extraction [12,33] SVM [96,97] and ANN [98,99]. It is known that the database characteristics can significantly affect the performance of a CAD scheme, or even a particular processing technique.…”

Section: Models Description Applicationmentioning

confidence: 99%

Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review

2020

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This paper provides a critical review of the literature on deep learning applications in breast tumor diagnosis using ultrasound and mammography images. It also summarizes recent advances in computer-aided diagnosis/detection (CAD) systems, which make use of new deep learning methods to automatically recognize breast images and improve the accuracy of diagnoses made by radiologists. This review is based upon published literature in the past decade (January 2010–January 2020), where we obtained around 250 research articles, and after an eligibility process, 59 articles were presented in more detail. The main findings in the classification process revealed that new DL-CAD methods are useful and effective screening tools for breast cancer, thus reducing the need for manual feature extraction. The breast tumor research community can utilize this survey as a basis for their current and future studies.

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“…This algorithm is also one of the lazy learning techniques. KNN is done by finding groups of k-value in the closest training data to objects on new data or testing data [14]. Fig.…”

Section: K Nearest Neighbormentioning

confidence: 99%

K-Value Effect for Detecting Stairs Descent using Combination GLCM and KNN

Johan

Utaminingrum

Budi

2020

JITeCS

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This study aims to analyze the k-value on K nearest neighbor classification. k-value is the distance used to find the closest data to label the class from the testing data. Each k-value can produce a different class label against the same testing data. The variants of k-value that we use are k=3, k=5 and k=7 to find the best k-value. There are 2 classes that are used in this research. Both classes are stairs descent and floor classes. The gray level co-occurrence matrix method is used to extract features. The data we use comes from videos obtained from the camera on the smart wheelchair taken by the frame. Refer to the results of our tests, the best k-value is obtained when using k=7 and angle 0° with accuracy is 92.5%. The stairs descent detection system will be implemented in a smart wheelchair

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Mammography classification using modified hybrid SVM-KNN

Cited by 22 publications

References 10 publications

A Robust Feature Extraction Technique for Breast Cancer Detection using Digital Mammograms based on Advanced GLCM Approach

A Robust Feature Extraction Technique for Breast Cancer Detection using Digital Mammograms based on Advanced GLCM Approach

Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review

K-Value Effect for Detecting Stairs Descent using Combination GLCM and KNN

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