Breast cancer detection in digital mammography using a novel hybrid approach of Salp Swarm and Cuckoo Search algorithm with deep belief network classifier

Abstract

ABSTRACT The Computer Aided Diagnosis (CAD) system has evolved as a useful tool for radiologists to classify breast cancer images into various categories, enabling early diagnosis and treatment. In CAD model construction, feature selection is essential for determining a subset of appropriate features to diagnose breast cancer. Salp Swarm Algorithm (SSA) is an evolutionary algorithm that simulates the swarming behaviour of salps. SSA has some advantages such as simplicity, speed in searching and ease of hybridization with other optimization algorithms. However, it suffers from being stuck in local optima and having slow convergence. To address these issues, this work proposes a novel hybridization algorithm called SSACS by combining the SSA with Cuckoo Search (CS) to improve convergence and exploitation capabilities. Further, the Deep Belief Network (DBN) classifier is applied to classify the mammogram images and improve the diagnosis rates. The proposed system’s efficacy is validated with the benchmark database of the Mammographic Image Analysis Society (mini-MIAS) dataset. The experimental findings indicate that the proposed SSACS with DBN classifier outperforms the state-of-the-art methods.