Brain Tumor Segmentation and Classification using hybrid Deep CNN with LuNet Classifier

Abstract

The early diagnosis of brain tumors is critical to enhancing patient survival and prospects. The magnetic resonance imaging (MRI) brain tumor images must be physically analyzed in this work. As a result, computerized approaches for more precise tumor diagnostics are required. However, evaluating shape, volume, borders, Tumor detection, size, segmentation, and classification remains challenging. This work proposes a hybrid Deep Convolutional Neural Network (DCNN) classifier using an enhanced LuNet classifier algorithm in this proposed work. The primary intention of this work is to determine the area of the tumor site and classify brain tumors as benign or malignant. Initially, we split the data using the extended LuNet algorithm. Grey-level co-occurrence matrix (GLCM) and VGG16 extracted the features, yielding 13 classification features. For pretreatment, the Laplacian of Gaussian filter (LOG) is used. Overall, the proposed approach tries to increase the performance of non-deep learning classifiers. Traditional classifiers are superior deep learning methods because they require fewer training data sets, have lower computing complexity, lower user costs, and are easier to use by people with less training experience. The proposed algorithm achieves a better accuracy rate of 99.7%. Compared to the existing algorithm, the proposed approach outperforms them.

MAEU‐NET: A novel supervised architecture for brain tumor segmentation

“…Segmentation of brain tumors plays an important role in detecting tumor cells. In the literature, there are many automated detection and classification algorithms like edge detection, contour/Atlas detection, ML, and deep learning techniques 15,17 – 28 are available. Although supervised learning methods achieve better performance, they rely on manual feature extraction and selection approaches.…”