A Robust Feature Extraction Technique for Breast Cancer Detection using Digital Mammograms based on Advanced GLCM Approach
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 earlystages so that the patient’s health can be improved.OBJECTIVES:...
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| Format: | Article |
| Language: | English |
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European Alliance for Innovation (EAI)
2022-03-01
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| Series: | EAI Endorsed Transactions on Pervasive Health and Technology |
| Subjects: | |
| Online Access: | https://eudl.eu/pdf/10.4108/eai.11-1-2022.172813 |
| _version_ | 1818315062965698560 |
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| author | L Kumari B Jagadesh |
| author_facet | L Kumari B Jagadesh |
| author_sort | L Kumari |
| collection | DOAJ |
| description | 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 earlystages 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 Co-occurrence 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) isused 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 NeuralNetworks (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. |
| first_indexed | 2024-12-13T08:59:34Z |
| format | Article |
| id | doaj.art-bae5cf3f52334ee6881b5e6f19145f8b |
| institution | Directory Open Access Journal |
| issn | 2411-7145 |
| language | English |
| last_indexed | 2024-12-13T08:59:34Z |
| publishDate | 2022-03-01 |
| publisher | European Alliance for Innovation (EAI) |
| record_format | Article |
| series | EAI Endorsed Transactions on Pervasive Health and Technology |
| spelling | doaj.art-bae5cf3f52334ee6881b5e6f19145f8b2022-12-21T23:53:12ZengEuropean Alliance for Innovation (EAI)EAI Endorsed Transactions on Pervasive Health and Technology2411-71452022-03-0183010.4108/eai.11-1-2022.172813A Robust Feature Extraction Technique for Breast Cancer Detection using Digital Mammograms based on Advanced GLCM ApproachL Kumari0B Jagadesh1Research Scholar, Department of Computer Science & Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur District, Andhra Pradesh, IndiaProfessor, Department of Computer Science & Engineering, Srinivasa Institute of Engineering and Technology, Amalapuram, Andhra Pradesh, IndiaINTRODUCTION: 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 earlystages 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 Co-occurrence 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) isused 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 NeuralNetworks (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.https://eudl.eu/pdf/10.4108/eai.11-1-2022.172813contrast limited adaptive histogram equalizationadvanced gray-level co-occurrence matrixk-nearest neighborartificial neural networkrandom forest and extreme gradient boosting |
| spellingShingle | L Kumari B Jagadesh A Robust Feature Extraction Technique for Breast Cancer Detection using Digital Mammograms based on Advanced GLCM Approach EAI Endorsed Transactions on Pervasive Health and Technology contrast limited adaptive histogram equalization advanced gray-level co-occurrence matrix k-nearest neighbor artificial neural network random forest and extreme gradient boosting |
| title | A Robust Feature Extraction Technique for Breast Cancer Detection using Digital Mammograms based on Advanced GLCM Approach |
| title_full | A Robust Feature Extraction Technique for Breast Cancer Detection using Digital Mammograms based on Advanced GLCM Approach |
| title_fullStr | A Robust Feature Extraction Technique for Breast Cancer Detection using Digital Mammograms based on Advanced GLCM Approach |
| title_full_unstemmed | A Robust Feature Extraction Technique for Breast Cancer Detection using Digital Mammograms based on Advanced GLCM Approach |
| title_short | A Robust Feature Extraction Technique for Breast Cancer Detection using Digital Mammograms based on Advanced GLCM Approach |
| title_sort | robust feature extraction technique for breast cancer detection using digital mammograms based on advanced glcm approach |
| topic | contrast limited adaptive histogram equalization advanced gray-level co-occurrence matrix k-nearest neighbor artificial neural network random forest and extreme gradient boosting |
| url | https://eudl.eu/pdf/10.4108/eai.11-1-2022.172813 |
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