Tabu Search and Machine-Learning Classification of Benign and Malignant Proliferative Breast Lesions

• Published in: BioMed research international Vol. 2020; no. 2020; pp. 1 - 10
• Main Authors: Al Maghayreh, Eslam, Mahmood, Awais, Rahmany, Ines, Dhahri, Habib, Elkilani, Wail
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 2020; Hindawi; John Wiley & Sons, Inc
• Subjects: Accuracy; Algorithms; Benign; Breast - diagnostic imaging; Breast - pathology; Breast cancer; Breast Neoplasms - classification; Breast Neoplasms - diagnosis; Breast Neoplasms - pathology; Cancer; Cell Proliferation; Classification; Data mining; Datasets; Deep learning; Diagnosis; Diagnosis, Computer-Assisted - methods; Diagnostic systems; Experiments; Female; Fibrocystic Breast Disease - classification; Fibrocystic Breast Disease - diagnosis; Fibrocystic Breast Disease - pathology; Fourier transforms; Gene expression; Humans; Image Interpretation, Computer-Assisted - methods; Learning algorithms; Lesions; Machine Learning; Mammography; Medical diagnosis; Metastasis; Morphology; Neoplasms - classification; Neoplasms - diagnosis; Neoplasms - pathology; Neural networks; Principal components analysis; Regression analysis; Review; Support vector machines; Tabu search; Tumors; Ultrasonic imaging; Wavelet transforms; Wisconsin
• ISSN: 2314-6133; 2314-6141; 2314-6141
• DOI: 10.1155/2020/4671349

Breast cancer is the most diagnosed cancer among women around the world. The development of computer-aided diagnosis tools is essential to help pathologists to accurately interpret and discriminate between malignant and benign tumors. This paper proposes the development of an automated proliferative breast lesion diagnosis based on machine-learning algorithms. We used Tabu search to select the most significant features. The evaluation of the feature is based on the dependency degree of each attribute in the rough set. The categorization of reduced features was built using five machine-learning algorithms. The proposed models were applied to the BIDMC-MGH and Wisconsin Diagnostic Breast Cancer datasets. The performance measures of the used models were evaluated owing to five criteria. The top performing models were AdaBoost and logistic regression. Comparisons with others works prove the efficiency of the proposed method for superior diagnosis of breast cancer against the reviewed classification techniques.