Novel Brain Tumor Classification Model with MLPNN Using UNET

Abstract

The detection and assessment of brain tumor are crucial for medical diagnosis. The MRI study supports radiologists and surgeons in the development of strategies for patient care in medical imaging. Accurate classification and segmentation of abnormal tissue over normal tissue are a key element. The proposed research work focused on an efficient Brain Tumor Segmentation and Classification approach named Multi-Feature Frequency Similarity Multi-Layer Perceptron Neural Network (MFFS-MLPNN). The proposed model considers both low- and high-grade features of glioblastoma present in MR images and varying features of tumors according to their size, contrast and texture. The MRI brain image is preprocessed by applying contrast curvature-based iterative shearlet filter and histogram equalization technique for enhancement. Followed by preprocessing, the denoised image has been segmented using the U-NET segmentation methodology. After segmentation, the tumors are extracted by using the cross-multi-linear local embedding method. The extracted features including Gray–White (GW) features were trained with multi-layer perception neural network for classification. The tumor has been classified into malignant and benign based on the result of multi-layer perceptron neural network. Based on the value of MFFS value, the algorithm performs classification. The effectiveness of the proposed MFFS-MPLNN algorithms has been verified over BRATS 2018 and 2019 datasets and compared with other state-of-the-art techniques. The experimental results show that average dice score is 85.47% for BRATS 2018 dataset whereas average dice score is 75.4% for BRATS 2019. It is observed that the dice score value is increased from 73.3% to 85.47% for BRATS 2018 and 64.3% to 75.4% for BRATS 2019 dataset. The average Hausdorff distance value has been decreased from 9.6% to 4.98% for BRATS 2018 dataset and 14.3% to 6.24% for BRATS 2019 dataset. Similarly, the sensitivity of the proposed algorithm has been increased from 70.7% to 89% and 61% to 82.1% for both BRATS 2018 and BRATS 2019 datasets, respectively.