Classification using deep learning neural networks for brain tumors

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.

Conclusion and future work: In this paper we proposed an efficient methodology which combines the discrete wavelet transform (DWT) with the Deep Neural Network (DNN) to classify the brain MRIs into Normal and 3 types of malignant brain tumors: glioblastoma, sarcoma and metastatic bronchogenic carcinoma. The new methodology architecture resemble the convolutional neural networks (CNN) architecture but requires less hardware specifications and takes a convenient time of processing for large size images (256 × 256). In addition using the DNN classifier shows high accuracy compared to traditional classifiers. The good results achieved using the DWT could be employed with the CNN in the future and compare the results.