IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

The Efforts of Deep Learning Approaches for Breast Cancer Detection Based on X-Ray Images

The Efforts of Deep Learning Approaches for Breast Cancer Detection Based on X-Ray Images
View Sample PDF
Author(s): Aras Masood Ismael (Sulaimani Polytechnic University, Iraq) and Juliana Carneiro Gomes (Universidade Federal de Pernambuco, Brazil)
Copyright: 2021
Pages: 20
Source title: Biomedical Computing for Breast Cancer Detection and Diagnosis
Source Author(s)/Editor(s): Wellington Pinheiro dos Santos (Universidade Federal de Pernambuco, Brazil), Washington Wagner Azevedo da Silva (Universidade Federal de Pernambuco, Brazil) and Maira Araujo de Santana (Universidade Federal de Pernambuco, Brazil)
DOI: 10.4018/978-1-7998-3456-4.ch013

Purchase

View The Efforts of Deep Learning Approaches for Breast Cancer Detection Based on X-Ray Images on the publisher's website for pricing and purchasing information.

Abstract

In this chapter, deep learning-based approaches, namely deep feature extraction, fine-tuning of pre-trained convolutional neural networks (CNN), and end-to-end training of a developed CNN model, are used to classify the malignant and normal breast X-ray images. For deep feature extraction, pre-trained deep CNN models such as ResNet18, ResNet50, ResNet101, VGG16, and VGG19 are used. For classification of the deep features, the support vector machines (SVM) classifier is used with various kernel functions namely linear, quadratic, cubic, and Gaussian, respectively. The aforementioned pre-trained deep CNN models are also used in fine-tuning procedure. A new CNN model is also proposed in end-to-end training fashion. The classification accuracy is used as performance measurements. The experimental works show that the deep learning has potential in detection of the breast cancer from the X-ray images. The deep features that are extracted from the ResNet50 model and SVM classifier with linear kernel function produced 94.7% accuracy score which the highest among all obtained.

Related Content

David Edson Ribeiro, Valter Augusto de Freitas Barbosa, Clarisse Lins de Lima, Ricardo Emmanuel de Souza, Wellington Pinheiro dos Santos. © 2021. 15 pages.
Juliana Carneiro Gomes, Maíra Araújo de Santana, Clarisse Lins de Lima, Ricardo Emmanuel de Souza, Wellington Pinheiro dos Santos. © 2021. 12 pages.
Maíra Araújo de Santana, Jessiane Mônica Silva Pereira, Clarisse Lins de Lima, Maria Beatriz Jacinto de Almeida, José Filipe Silva de Andrade, Thifany Ketuli Silva de Souza, Rita de Cássia Fernandes de Lima, Wellington Pinheiro dos Santos. © 2021. 19 pages.
Jessiane Mônica Silva Pereira, Maíra Araújo de Santana, Clarisse Lins de Lima, Rita de Cássia Fernandes de Lima, Sidney Marlon Lopes de Lima, Wellington Pinheiro dos Santos. © 2021. 25 pages.
Adriel dos Santos Araujo, Roger Resmini, Maira Beatriz Hernandez Moran, Milena Henriques de Sousa Issa, Aura Conci. © 2021. 35 pages.
Abir Baâzaoui, Walid Barhoumi. © 2021. 21 pages.
Marcus Costa de Araújo, Luciete Alves Bezerra, Kamila Fernanda Ferreira da Cunha Queiroz, Nadja A. Espíndola, Ladjane Coelho dos Santos, Francisco George S. Santos, Rita de Cássia Fernandes de Lima. © 2021. 44 pages.
Body Bottom