A Hybrid Genetic Algorithm Model for Early Detection and Classification of Breast Tumours.

Abstract

The severity and fatality prevalence of breast cancer remain a global issue. The existing diagnosis and treatment are not efficient enough to successfully detect breast cancer tumours at an early stage. According to recent studies, breast cancer causes 25.84% of all cancer-related fatalities. New breast cancer cases account for 29.46% and 31.85% of all new cancer cases in Africa and Ethiopia, respectively. In Ethiopia, 72.56% of breast cancer diagnoses are in advanced stages (95% CI: 68.46-76.65%), emphasizing the critical need for early detection. Furthermore, a lack of radiologists causes delays in the annual reading and classification of Breast Imaging Reporting and Data Systems (BI-RADS). To address the problem areas of early detection, false positives and false negatives, research efforts continue to present different solutions using different advanced techniques. The techniques used are artificial intelligence (AI), machine learning (ML), and Genetic Algorithms (GA). Artificial intelligence and Machine learning are crucial in improving breast cancer diagnosis to reduce late discovery, which significantly affects survival chances. In this study, we collected 4092 mammographic image data from the diagnosis center with the radiologist’s recommendation for the diagnosis and future patient steps. In addition to this, we use the Wisconsin breast cancer public image data for training, validation, and testing of the model. We work on a hybrid model using genetic algorithms and machine learning for early detection and classification of the stage and type of breast cancer tumours to reduce the outliers for false-positive and false negative results. The implemented models are hybridizing Genetic Algorithms with K Nearest Neighbour (K-NN) and Support Vector Machine (SVM), (GA-KNN-SVM), a modified GA with Pontraygin Minimum Principles (PMP), and a hybrid GA model with K-Means++. The GAKNN-SVM model achieves superior performance in breast tumour detection, utilizing the Wisconsin Breast Cancer Diagnostic Dataset. The results show significant improvements, with accuracy, sensitivity, and specificity rates reaching 98.25%, 98.15%, and 98.41%, respectively, of the test sample data. At last, we have outperformed results in detecting breast tumours and classified mammography image data by BI-RADS scoring.