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Optimal Feature Selection for Computer-Aided Characterization of Tissues: Case Study of Mammograms

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Intelligent Healthcare

Abstract

Feature extraction and selection are very important stages in pattern recognition and computer vision solutions with far-reaching effects on their performance. In computer-aided diagnosis (CADx) systems, efficiency is affected by its subjectivity to the accuracy of the region of interest (ROI) extraction technique, which is largely dependent on the features extracted. Optimization algorithms are often used to improve the selection of discriminative features which thereby leads to improve accuracy of the CADx systems. This work considers the effects of optimizing selected features in the performance of breast tissue characterization in mammograms. It uses Whale Optimization Algorithm (WOA) to optimize Otsu fitness function of Gray Level Co-occurrence Matrix (GLCM) in extracting the region of interest (ROI). The extracted features were classified into BIRADS scales 1, 2 and 5 using Multiclass Support Vector Machine (MSVM). The performance of the developed algorithm was evaluated using specificity, sensitivity as well as accuracy and compared with other techniques namely Texture Signature (TS), Pixel-Based Morphological (PBM), Natural Language Processing (NLP), and Interactive Data Language (IDL). The result of the developed WOA-Otsu-GLCM-MSVM CADx algorithm for specificity, sensitivity, and accuracy are 96%, 92% and 94%, respectively. The developed algorithm gave an accuracy of 94.4% as against 81.0%, 85.7%, 93.0% and 82.5% for TS, PBM, NLP and IDL methods, respectively. The characterization of the breast tumour using the developed CADx algorithm performed better compared with the conventional methods.

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Authors and Affiliations

  1. Department of Electronic and Electrical Engineering, Ladoke Akintola University of Technology (LAUTECH), Ogbomoso, Nigeria

    John Adedapo Ojo, Peter Olalekan Idowu & Ifeoluwa David Solomon

  2. Department of Radiology, LAUTECH, Ogbomoso, Nigeria

    Temitope Olugbenga Bello

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  1. John Adedapo Ojo
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  2. Temitope Olugbenga Bello
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  3. Peter Olalekan Idowu
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  4. Ifeoluwa David Solomon
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Correspondence to Peter Olalekan Idowu .

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Editors and Affiliations

  1. Electronics & Communication Engineering, Birla Institute of Technology, Ranchi, Jharkhand, India

    Chinmay Chakraborty

  2. Computer Engineering, Persian Gulf University, Bushehr, Iran

    Mohammad R. Khosravi

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Ojo, J.A., Bello, T.O., Idowu, P.O., Solomon, I.D. (2022). Optimal Feature Selection for Computer-Aided Characterization of Tissues: Case Study of Mammograms. In: Chakraborty, C., Khosravi, M.R. (eds) Intelligent Healthcare. Springer, Singapore. https://doi.org/10.1007/978-981-16-8150-9_3

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  • DOI: https://doi.org/10.1007/978-981-16-8150-9_3

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