Abstract
Feature Selection is the vibrant phase in Computer Aided Diagnosis Systems (CAD) to classify breast cancer medical images. Mammogram images are best for breast cancer screening which helps in early detection of the disease in the women. The main objective is to select the better features to increase the classification performance. To achieve this, we used a 4-step process: 1. preprocessing 2. feature extraction, 3. feature selection, 4. classification. Initially, the medical images are acquired and preprocessed using Contrast-Limited Histogram Equalization (CLAHE) and the features are retrieved using Advanced Gray-Level Co-occurrence Matrix (AGLCM) which extracts texture, shape and intensity-based features. Then feature selection is applied to obtain the better features. Toachieve this, we proposed a new feature selection technique called Weighted Adaptive Binary Teaching Learning Based Optimization (WA-BTLBO) and fitness function used is the classification accuracy. The selected features are trained and tested using XGBoost classifier and the results are compared with other classifiers namely K-Nearest Neighbor (KNN), Random Forest (RF), Artificial Neural Networks (ANN), andSupport Vector Machines (SVM). The experiments are done using publicly available mammogram medical images namely Mammographic Image Analysis Society (MIAS). Theresults show that WA-BTLBO with XGBoost classifier is superior to other feature selection techniques namelyParticle Swarm Optimization (PSO) and Binary TLBO (BTLBO) and other state-of-art methods in classifying MIAS mammogram images into normal or abnormal. This paper helps the physiologists and radiologists to detect the breast cancer in women so that life span of the patient can be increased. In future, we extend our work to use other metaheuristic methods like firefly algorithm, biogeography-based optimization and other algorithms to select the optimal features and also to apply on large databases and on other types of diseases.
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References
Beura S et al (2015) Mammogram classification using two dimensional discrete wavelet transform and gray-level co-occurrence matrix for detection of breast cancer. Neurocomputing 122:1–14
Bhardwaj H, Sakalle A, Tiwari A, Verma M, Bhardwaj A (2018) Breast cancer diagnosis using simultaneous feature selection and classification: a genetic programming approach. IEEE symposium series on computational intelligence, Bangalore, pp 2186–2192
Bingham E, Mannila H (2001) Random projection in dimensionality reduction: applications to image and text data. International conference on Knowledge discovery and data mining 245–250
Calas MJG, Gutfilen B, de A. Pereira WC (2012) CAD and mammography: Why use this tool? Radiol Bras 45:46–52
Chakraborty J, Mukhopadhyay S, Singla V, Khandelwal N, Rangayyan RM (2012) Detection of masses in mammograms using region growing controlled by multilevel thresholding. IEEE Symp Comput Med Syst 25:1–6
Chen X, Xu B, Yu K, Du W (2018) Teaching-learning-based optimization with learning enthusiasm and its application in chemical engineering. J Mech Appl Math
Dabass J, Hanmandlu M, Vig R (2020) Classification of digital mammograms using information set features and Hanman Transform based classifiers. Inform Med Unlocked 20:2020
Dheeba J, Selvi ST (2012) A swarm optimized neural network system for classification of microcalcification in mammograms. J Med Syst. https://doi.org/10.1007/s10916-011-9781-3
Dhiman G, Vinoth Kumar V, Kaur A, Sharma A (2021) DON: deep learning and optimization-based framework for detection of novel coronavirus disease using X-ray images. Interdiscip Sci: Comput Life Sci 13(2):260–272. https://doi.org/10.1007/s12539-021-00418-7
Frejlichowski D, Gościewska K (2012) Application of 2D Fourier descriptors and similarity measures to the general shape analysis problem. International conference on computer vision and graphics, pp 371–378
Gherghout Y, Tlili Y, Souici L (2019) Classification of breast mass in mammography using anisotropic diffusion filter by selecting and aggregating morphological and textural features. Evol Syst
Gupta B, Tiwari M (2017) A tool supported approach for brightness preserving contrast enhancement and mass segmentation of mammogram images using histogram modified grey relational analysis. Multidimens Syst Signal Process 28
Guyon AE (2003) An introduction to variable and feature selection. J Mach Learn Res 1157–1182
Hossam A, Harb H, Kader H (2018) A suboptimum feature selection algorithm for effective breast cancer detection based on particle swarm optimization. IOSR J Electron Commun Eng 13:01–12
https://machinelearningmastery.com/gentle-introduction-xgboost-applied-machine-learning/. Accessed 9 Sep 2020
Jagadesh BN, Kanya Kumari L (2021) A GLCM based feature extraction in mammogram images using machine learning algorithms. Int J Current Res Rev 13:145–149
Jona J, Nagaveni N (2012) A hybrid swarm optimization approach for feature set reduction in digital mammograms. WSEAS Trans Inf Sci Appl 9:340–349
Kalyani G, Janakiramaiah B (2021) Deep learning-based detection and classification of adenocarcinoma cell nuclei. In: Hybrid computational intelligence for pattern analysis, trends in deep learning methodologies, Academic Press, pp 241–264
Kalyani G, Janakiramaiah B, Karuna A et al (2021) Diabetic retinopathy detection and classification using capsule networks. Complex Intell Syst
Kanya Kumari L, Jagadesh BN (2020) A novel approach for detection of tumors in mammographic images using fourier descriptors and KNN. Lecture Notes in Electrical Engineering, pp 1877–1884
Khana S, Hussainb M, Aboalsamhb H, Mathkourb H, Bebisc G, Zakariahd M (2016) Optimized Gabor features for mass classification in mammography. Appl Soft Comput 267–280
Khehra BS, Pharwaha APS (2017) Comparison of genetic algorithm, particle swarm optimization and biogeography-based optimization for feature selection to classify clusters of microcalcifications. J Inst Eng India Ser 98:189–202
Li J, Cheng K, Wang S, Morstatter F, Trevino RP, Tandg J, Liu H (2018) Feature selection: a data perspective. ACM Comput Surv 50
Mafarja M, Mirjalili S (2017) Hybrid whale optimization algorithm with simulated annealing for feature selection. Neurocomputing 260:302–312
Mafarja M, Eleyan D, Jaber I, Hammouri A, Mirjalili S (2017) Binary Dragonfly Algorithm for feature selection. International conference on new trends in computing sciences 12–17
Maitra IK, Nag S, Bandyopadhyay SK (2012) Technique for preprocessing of digital mammogram. Comput Methods Programs Biomed 107
Mohan A, Nandhini M (2018) Optimal feature selection using binary teaching learning based optimization algorithm. J King Saud Univ—Comput Inf Sci
Mohan A, Nandhini M (2020) Wrapper based Feature Selection using Integrative Teaching Learning Based Optimization Algorithm. Int Arab J Inf Technol 17
Mohanty F, Rup S, Dash B et al (2019) Mammogram classification using contourlet features with forest optimization-based feature selection approach. Multimed Tools Appl 78:12805–12834
Parekh R (2012) Using texture analysis for medical diagnosis. IEEE MultiMed19:28–37
Ramani R, Vanitha S, Valarmathy S (2013) “The pre-processing techniques for breast cancer detection in mammography images”, International Journal of Image. Graphics and Signal Processing 5:47–54
Rao RV, Savsani VJ, Vakharia DP (2012) Teaching-learning-based optimization: an optimization method for continuous non-linear large-scale problems. Inf Sci 183:1–15
Satapathy SC, Naik A, Parvathi K (2013) Weighted Teaching-learning-based optimization for global function optimization. Appl Math 429–439
Shahbeig S, Helfroush M, Rahideh A (2016) A fuzzy multi-objective hybrid TLBO-PSO approach to select the associated genes with breast cancer. Signal Process 131:58–65
Shankar T, Ranjana I (2020) Classification of masses in digital mammograms using Biogeography-based optimization technique. J King Saud Univ—Comput Inf Sci 32:1140–1148
Shankar T, Ranjana I (2020) Classification of masses in digital mammograms using the genetic ensemble method. J Intell Syst 29:831–845
Song R, Li T, Wang Y (2020) Mammographic classification based on XGBoost and DCNN with multi features. IEEE Access 8:75011–75021
Suckling J, Parker J, Dance D, Astley S, Hutt I, Boggis C, Ricketts I et al (2015) Mammographic Image Analysis Society (MIAS) database v1.21 [Dataset]. https://www.repository.cam.ac.uk/handle/1810/250394
Sudha MN, Selvarajan S (2016) Feature selection based on enhanced Cuckoo search for breast cancer classification in mammogram image. https://doi.org/10.4236/cs.2016.74028
Venkata Rao R, Patel V (2013) An improved teaching-learning-based optimization algorithm for solving unconstrained optimization problems. Sharif University of Technology- Scientia Iranica D. 20, 710–720
Zhang Y, Wu X, Lu S, Wang H, Phillips P, Wang S (2016) Smart detection on abnormal breasts in digital mammography based on contrast-limited adaptive histogram equalization and chaotic adaptive real-coded biogeography-based optimization. Simulation 92:873–885
Zhang Z, Yang P (2008) An ensemble of classifiers with genetic algorithm based feature selection. IEEE Intell Inf Bull 9:18–24
Zyout I, Czajkowska J, Grzegorzek M (2015) Multi-scale textural feature extraction and particle swarm optimization-based model selection for false positive reduction in mammography. Comput Med Imaging Graph 46:95–107
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This work was supported by the Mammographic Image Analysis Society with authors contributing their time and facilities free-of-charge [grant number RNAG/302].
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Kanya Kumari, L., Naga Jagadesh, B. An adaptive teaching learning based optimization technique for feature selection to classify mammogram medical images in breast cancer detection. Int J Syst Assur Eng Manag 15, 35–48 (2024). https://doi.org/10.1007/s13198-021-01598-7
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DOI: https://doi.org/10.1007/s13198-021-01598-7