Bio-Inspired Optimization Technique for Feature Selection to Enhance Accuracy of BC Detection

The common type of cancer that results in death across the world is Breast Cancer (BC). It is necessary to detect cancer in its earlier stages when it is more treatable and can be effectively managed The detection of BC can be carried out by employing a variety of different Machine Learning (ML) approaches in the diagnostic process. This study proposes a ML-based strategy for doing automated BC analysis. There are several steps in tumor detection, and feature extraction (FE) is one of them. The tumor condition's existence in an image can be determined using the powerful Gray Level Co-occurrence Matrix (GLCM) feature descriptor identification approach, in addition to the Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) Feature Selection (FS) techniques are employed Techniques from the realm of ML, such as Support Vector Machine (SVM), Naive Bayes (NB), and Random Forest (RF) algorithm, are used throughout the data training and testing phases for tumor classification. The outcome of both optimized FS techniques is given to the ML models for identifying BC. From the experimental result, it is identified that the ACO with SVM gives greater accuracy of 97.4% than all other techniques.