Breast Cancer Diagnosis using Artificial Neural Networks with Extreme Learning Techniques

Abstract

Abstract-Breast cancer is the second cause of dead among women. Early detection followed by appropriate cancer treatment can reduce the deadly risk. Medical professionals can make mistakes while identifying a disease. The help of technology such as data mining and machine learning can substantially improve the diagnosis accuracy. Artificial Neural Networks (ANN) has been widely used in intelligent breast cancer diagnosis. However, the standard Gradient-Based Back Propagation Artificial Neural Networks (BP ANN) has some limitations. There are parameters to be set in the beginning, long time for training process, and possibility to be trapped in local minima. In this research, we implemented ANN with extreme learning techniques for diagnosing breast cancer based on Breast Cancer Wisconsin Dataset. Results showed that Extreme Learning Machine Neural Networks (ELM ANN) has better generalization classifier model than BP ANN. The development of this technique is promising as intelligent component in medical decision support systems.

Breast cancer diagnosis using the fast learning network algorithm

“…The accuracy results of most prior works, such as ( 31 – 34 ), are still not encouraging and need more enhancement.…”

“…The research in ( 31 ) has proposed the ELM-ANN (extreme learning machine-artificial neural networks) approach for diagnosing the BC. The proposed ELM-ANN approach has been assessed based on the WBCD.…”

Identification of Breast Tumor Using Hybrid Approach of Independent Component Analysis and Deep Neural Network

“…and achieved accuracy of 97.6% with their method. On a WBC dataset, the authors of [8] utilized ANN with Extreme Learning Machine and a 5-fold cross validation technique. The performance of ELM with conventional BP ANN with gradient descent based learning algorithms are compared.…”

Study on Different Approaches for Breast Cancer Detection: A Review

“…B. Monica Jenifer et al [4] in their paper discussed efficient image processing methods for breast cancer detection. It follows the procedure such as a. Tumor enhancement, b. Tumor segmentation, c. Extraction of properties from the segmented tumor region, d. Utilization of SVM classifier.…”

“…Abien Fred M. Agarap [18] presents a comparison among six machine learning (ML) algorithms which includes GRU-SVM [4], Linear Regression, Nearest Neighbor (NN) search, Multilayer Perceptron (MLP), Softmax Regression and Support Vector Machine (SVM) on the Wisconsin Diagnostic Breast Cancer (WDBC) dataset by measuring their classification test accuracy, and their specificity and sensitivity values. For the implementation of the ML algorithms, the dataset was partitioned as 70% for training phase and 30% for the testing phase.…”