Work

Classification using deep learning neural networks for brain tumors

Year: 2017

Type: article

Abstract: Deep Learning is a new machine learning field that gained a lot of interest over the past few years. It was widely applied to several applications and proven to be a powerful machine learning tool for many of the complex problems. In this paper we used Deep Neural Network classifier which is one of the DL architectures for classifying a dataset of 66 brain MRIs into 4 classes e.g. normal, glioblastoma, sarcoma and metastatic bronchogenic carcinoma tumors. The classifier was combined with the discrete wavelet transform (DWT) the powerful feature extraction tool and principal components analysis (PCA) and the evaluation of the performance was quite good over all the performance measures.

Source: Future Computing and Informatics Journal

DOI: 10.1016/j.fcij.2017.12.001

Authors: Heba Mohsen, El-Sayed A. El-Dahshan, El-Sayed M. El-Horbaty, Abdel-Badeeh M. Salem

Institutions: Future University in Egypt, Ain Shams University, Egyptian e-Learning University

Language: English

Topic: Brain Tumor Detection and Classification

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

OpenAlex ID: W2780099243

FWCI: 21.26

Cites: 13

Cited by: 1,113

Related to: 10