Open AccessA New Modified Recurrent Extreme Learning with PSO Machine Based on Feature Fusion with CNN Deep Features for Breast Cancer Detection
Author(s) -
Surendra Prasad M,
Kombadipatti Hospitals. Salem Kochi Highway,
S. Manimurugan
Publication year - 2020
Publication title -
journal of computational science and intelligent technologies
Language(s) - English
Resource type - Journals
ISSN - 2582-9041
DOI - 10.53409/mnaa.jcsit20201303
Subject(s) - artificial intelligence , breast cancer , computer science , convolutional neural network , deep learning , extreme learning machine , cad , pattern recognition (psychology) , classifier (uml) , machine learning , cluster analysis , feature (linguistics) , feature extraction , mammography , artificial neural network , cancer , medicine , linguistics , philosophy , engineering drawing , engineering
Breast cancer is a prevalent cause of death, and is the only form of cancer that is common among women worldwide and mammograms-based computer-aided diagnosis (CAD) program that allows early detection, diagnosis and treatment of breast cancer. But the performance of the current CAD systems is still unsatisfactory. Early recognition of lumps will reduce overall breast cancer mortality. This study investigates a method of breast CAD, focused on feature fusion with deep features of the Convolutional Neural Network (CNN). First, present a scheme of mass detection based on CNN deep features and modified clustering of the Extreme Learning Machine (MRELM). It forecasts load through Recurrent Extreme Learning Machine (RELM) and utilizes Artificial Bee Colony (ABC) to optimize weights and biases. Second, a collection of features is constructed that relays deep features, morphological features, texture features, and density features. Third, MRELM classifier is developed to distinguish benign and malignant breast masses using the fused feature set. Extensive studies show the precision and efficacy of the proposed method of mass diagnosis and classification of breast cancer.