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According to cancer.org news in 2018, the most common cancer diagnosed in women is breast, lung, and colorectal cancers. About 30% of all new cancer diagnoses in women refer to breast cancer. Therefore, predicting breast cancer in its early stages stays a controversial challenge. In this study, we focus on DNA methylation gene expression profiles of patients (series GSE32393 NCBI dataset). For dimension reduction, Non-negative matrix factorization (NMF) is employed and combined with a new method called column-splitting. The reason of superiority of NMF algorithm over other popular ones is its usage for both supervised and unsupervised feature matrix transformation into lower dimensional feature matrix. Afterward the main algorithms that are used for classification are extreme learning machine and support vector machine algorithms. The achieved prediction performances are comparable to deep learning models. The best-attained performance has zero error rate on NCBI 137 samples. While it has shown that, the best deep model on the mentioned data has 2.7 error rate.

Tumor identification is one of the main and most influential factors in the identification of the type of treatment, the treatment process, the success rate of treatment and the follow-up of the disease. Convolution neural networks are one of the most important and practical classes in the field of deep learning and feed-forward neural networks that is highly applicable for analyzing visual imagery. CNNs learn the features extracted by the convolution and maxpooling layers. Extreme Learning Machines (ELM) are a kind of learning algorithm that consists of one or more layers of hidden nodes. These networks are used in various fields such as classification and regression. By using a CNN, this paper tries to extract hidden features from images. Then a kernel ELM (KELM) classifies the images based on these extracted features. In this work, we will use a dataset to evaluate the effectiveness of our proposed method, which consists of three types of brain tumors including meningioma, glioma and pituitary tumor in T1-weighted contrast-enhanced MRI (CE-MRI) images. The results of this ensemble of CNN and KELM (KE-CNN) are compared with different classifiers such as Support Vector Machine, Radial Base Function, and some other classifiers. These comparisons show that the KE-CNN has promising results for brain tumor classification.