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

Aras Masood Ismael, Juliana Carneiro Gomes
Copyright: © 2021 |Pages: 20
DOI: 10.4018/978-1-7998-3456-4.ch013
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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.
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Artificial Neural Network (ANN) can be extensively used to analyze breast Infection and develop accurate diagnostics about the infections. Most researchers have been using ANN to develop solutions that enable medical practitioners too accurately and diagnose breast Infections. During developing optimal solutions, it’s mandatory for computer scientists to determine the best log sigmoid activation functions, the series of layers (input and hidden layers) and Multi-Layer Perceptron’s (MLP).

Umer et al (2019) In their study, they used several algorithms to diagnose Breast Disease Such as k nearest neighbors, Support vector machine, Decision tree and Naïve bayes, Among the algorithms used, the Sport Vector Machine obtained the best results from which all other approaches, the data used in the study were collected at a hospital in Kashmir, the highest test accuracy achieved in this study was 98.89% .

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