A Review on Brain Tumor Diagnosis from MRI Images : Practical Implications, Key Achievements, and Lessons Learned

Medical imaging plays an integral role in the identification, segmentation, and classification of brain tumors. The invention of MRI has opened new horizons for brain-related research. Recently, researchers have shifted their focus towards applying digital image processing techniques to extract, analyze and categorize brain tumors from MRI. Categorization of brain tumors is defined in a hierarchical way moving from major to minor ones. A plethora of work could be seen in literature related to the classification of brain tumors in categories such as benign and malignant. However, there are only a few works reported on the multiclass classification of brain images where each part of the image containing tumor is tagged with major and minor categories. The precise classification is difficult to achieve due to ambiguities in images and overlapping characteristics of different type of tumors. In the current study, a comprehensive review of recent research on brain tumors multiclass classification using MRI is provided. These multiclass classification studies are categorized into two major groups: XX and YY and each group are further divided into three sub-groups. A set of common parameters from the reviewed works is extracted and compared to highlight the merits and demerits of individual works. Based on our analysis, we provide a set of recommendations for researchers and professionals working in the area of brain tumors classification.

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Computer-assisted brain tumor type discrimination using magnetic resonance imaging features.

Accurate tumor segmentation is an essential and crucial step for computer-aided brain tumor diagnosis and surgical planning. Subjective segmentations are widely adopted in clinical diagnosis and treating, but they are neither accurate nor reliable. An automatical and objective system for brain tumor segmentation is strongly expected. But they are still facing some challenges such as lower segmentation accuracy, demanding a priori knowledge or requiring the human intervention. In this paper, a novel and new coarse-to-fine method is proposed to segment the brain tumor. This hierarchical framework consists of preprocessing, deep learning network based classification and post-processing. The preprocessing is used to extract image patches for each MR image and obtains the gray level sequences of image patches as the input of the deep learning network. The deep learning network based classification is implemented by a stacked auto-encoder network to extract the high level abstract feature from the input, and utilizes the extracted feature to classify image patches. After mapping the classification result to a binary image, the post-processing is implemented by a morphological filter to get the final segmentation result. In order to evaluate the proposed method, the experiment was applied to segment the brain tumor for the real patient dataset. The final performance shows that the proposed brain tumor segmentation method is more accurate and efficient.

A deep learning-based segmentation method for brain tumor in MR images

SDCT-AuxNetθ: DCT augmented stain deconvolutional CNN with auxiliary classifier for cancer diagnosis.

The incidence of brain tumors is increasing rapidly particularly in the young generation. Tumors can directly destroy all healthy brain cells. Manual (Physical) classification can cause human error. Automatic classification method is required because it reduces the load on the human observer, accuracy is not affected due to large number of images. This paper elaborates attempt to detection & classification of tumor in benign stage. The proposed method consists of two stages namely feature extraction and classification. In the first stage, obtained the features related to MRI images using Gray Level Cooccurrence Matrix (GLCM) based methods, this is one of the tools for extracting texture features and second stage, the classifier is classified images using K-nearest neighbour (K -NN) classifier.

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Detection and Classification of Brain Tumors

In this paper, a new method for Brain Tumor Classification using Probabilistic Neural Network with Discrete Cosine Transformation is proposed. The conventional method for computerized tomography and magnetic resonance brain images classification and tumor detection is by human inspection. Operator assisted classification methods are impractical for large amounts of data and are also non reproducible. Computerized Tomography and Magnetic Resonance images contain a noise caused by operator performance which can lead to serious inaccuracies in classification. The use of Neural Network techniques shows great potential in the field of medical diagnosis. Hence, in this paper the Probabilistic Neural Network with Discrete Cosine Transform was applied for Brain Tumor Classification. Decision making was performed in two steps, i) Dimensionality reduction and Feature extraction using the Discrete Cosine Transform and ii) classification using Probabilistic Neural Network (PNN). Evaluation was performed on image data base of 20 Brain Tumor images. The proposed method gives fast and better recognition rate when compared to previous classifiers. The main advantage of this method is its high speed processing capability and low computational requirements.

Brain Tumor Classification using Discrete Cosine Transform and Probabilistic Neural Network

In this paper, a new Face Recognition method based on Two Dimensional Discrete Cosine Transform with Linear Discriminant Analysis (LDA) and K Nearest neighbours (KNN) classifier is proposed. This method consists of three steps, i) Transformation of images from special to frequency domain using Two dimensional discrete cosine transform ii) Feature extraction using Linear Discriminant Analysis and iii) classification using K Nearest Neighbour  classifier. Linear Disceminant Analysis searches the directions for maximum discrimination of classes in addition to dimensionality reduction. Combination of Two Dimensional   Discrete Cosine transform and Linear Discriminant Analysis is used for improving the capability of Linear Discriminant Analysis when few samples of images are available. K Nearest Neighbour classifier gives fast and accurate classification of face images that makes this method useful in online applications. Evaluation was performed on two face data bases. First database of 400 face images from AT&T face database, and the second database of thirteen students are taken. The proposed method gives fast and better recognition rate when compared to other classifiers. The main advantage of this method is its high speed processing capability and low  computational requirements in terms of both speed and memory utilizations. DOI: http://dx.doi.org/10.11591/ij-ai.v1i4.767

https://ijai.iaescore.com/index.php/IJAI/article/download/1295/1186

D. Sridhar

Papers

Brain Tumor Classification using Discrete Cosine Transform and Probabilistic Neural Network

Face Recognition Using Two Dimensional Discrete Cosine Transform, Linear Discriminant Analysis And K Nearest Neighbor Classifier