A CNN Hyperparameters Optimization Based on Particle Swarm Optimization for Mammography Breast Cancer Classification

Author:

Aguerchi Khadija1,Jabrane Younes1ORCID,Habba Maryam2,El Hassani Amir Hajjam3ORCID

Affiliation:

1. MSC Laboratory, Cadi Ayyad University, Marrakech 40000, Morocco

2. National School of Applied Sciences of Safi, Cadi Ayyad University, Safi 46000, Morocco

3. Nanomedicine Imagery & Therapeutics Laboratory, EA4662—Bourgogne-Franche-Comté University, 90010 Belfort, France

Abstract

Breast cancer is considered one of the most-common types of cancers among females in the world, with a high mortality rate. Medical imaging is still one of the most-reliable tools to detect breast cancer. Unfortunately, manual image detection takes much time. This paper proposes a new deep learning method based on Convolutional Neural Networks (CNNs). Convolutional Neural Networks are widely used for image classification. However, the determination process for accurate hyperparameters and architectures is still a challenging task. In this work, a highly accurate CNN model to detect breast cancer by mammography was developed. The proposed method is based on the Particle Swarm Optimization (PSO) algorithm in order to look for suitable hyperparameters and the architecture for the CNN model. The CNN model using PSO achieved success rates of 98.23% and 97.98% on the DDSM and MIAS datasets, respectively. The experimental results proved that the proposed CNN model gave the best accuracy values in comparison with other studies in the field. As a result, CNN models for mammography classification can now be created automatically. The proposed method can be considered as a powerful technique for breast cancer prediction.

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Computer Graphics and Computer-Aided Design,Computer Vision and Pattern Recognition,Radiology, Nuclear Medicine and imaging

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