The aim of this work is to develop a Computer-Aided-Brain-Diagnosis (CABD) system that can determine if a brain scan shows signs of Alzheimer’s disease. The method utilizes Magnetic Resonance Imaging (MRI) for classification with several feature extraction techniques. MRI is a non-invasive procedure, widely adopted in hospitals to examine cognitive abnormalities. Images are acquired using the T2 imaging sequence. The paradigm consists of a series of quantitative techniques: filtering, feature extraction, Student’s t-test based feature selection, and k-Nearest Neighbor (KNN) based classification. Additionally, a comparative analysis is done by implementing other feature extraction procedures that are described in the literature. Our findings suggest that the Shearlet Transform (ST) feature extraction technique offers improved results for Alzheimer’s diagnosis as compared to alternative methods. The proposed CABD tool with the ST + KNN technique provided accuracy of 94.54%, precision of 88.33%, sensitivity of 96.30% and specificity of 93.64%. Furthermore, this tool also offered an accuracy, precision, sensitivity and specificity of 98.48%, 100%, 96.97% and 100%, respectively, with the benchmark MRI database.

Automated Detection of Alzheimer’s Disease Using Brain MRI Images– A Study with Various Feature Extraction Techniques

Brain tumor classification based on DWT fusion of MRI sequences using convolutional neural network

Brain tumor diagnosis and classification still rely on histopathological analysis of biopsy specimens today. The current method is invasive, time-consuming and prone to manual errors. These disadvantages show how essential it is to perform a fully automated method for multi-classification of brain tumors based on deep learning. This paper aims to make multi-classification of brain tumors for the early diagnosis purposes using convolutional neural network (CNN). Three different CNN models are proposed for three different classification tasks. Brain tumor detection is achieved with 99.33% accuracy using the first CNN model. The second CNN model can classify the brain tumor into five brain tumor types as normal, glioma, meningioma, pituitary and metastatic with an accuracy of 92.66%. The third CNN model can classify the brain tumors into three grades as Grade II, Grade III and Grade IV with an accuracy of 98.14%. All the important hyper-parameters of CNN models are automatically designated using the grid search optimization algorithm. To the best of author’s knowledge, this is the first study for multi-classification of brain tumor MRI images using CNN whose almost all hyper-parameters are tuned by the grid search optimizer. The proposed CNN models are compared with other popular state-of-the-art CNN models such as AlexNet, Inceptionv3, ResNet-50, VGG-16 and GoogleNet. Satisfactory classification results are obtained using large and publicly available clinical datasets. The proposed CNN models can be employed to assist physicians and radiologists in validating their initial screening for brain tumor multi-classification purposes.

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Multi-Classification of Brain Tumor MRI Images Using Deep Convolutional Neural Network with Fully Optimized Framework

Brain tumor represents one of the most fatal cancers around the world. It is common cancer in adults and children. It has the lowest survival rate and various types depending on their location, texture, and shape. The wrong classification of the tumor brain will lead to bad consequences. Consequently, identifying the correct type and grade of tumor in the early stages has an important role to choose a precise treatment plan. Examining the magnetic resonance imaging (MRI) images of the patient’s brain represents an effective technique to distinguish brain tumors. Due to the big amounts of data and the various brain tumor types, the manual technique becomes time-consuming and can lead to human errors. Therefore, an automated computer assisted diagnosis (CAD) system is required. The recent evolution in image classification techniques has shown great progress especially the deep convolution neural networks (CNNs) which have succeeded in this area. In this regard, we exploited CNN for the problem of brain tumor classification. We suggested a new model, which contains various layers in the aim to classify MRI brain tumor. The proposed model is experimentally evaluated on three datasets. Experimental results affirm that the suggested approach provides a convincing performance compared to existing methods.

Deep CNN for Brain Tumor Classification

DualCheXNet: dual asymmetric feature learning for thoracic disease classification in chest X-rays

A hybrid feature extraction approach for brain MRI classification based on Bag-of-words

The use of rapid prototyping tools such as MATLAB- Simulink and Xilinx System Generator becomes increasingly important because of time-to-market constraints. This paper presents a methodology for implementing real-time DSP applications on a reconfigurable logic platform using Xilinx System Generator (XSG) for Matlab. The methodology aims to improve the design verfication efficiency for such complex system. It presents an architecture for Color Space Conversion (CSC) RGBTOYCbCr for video processing using Xilinx System Generator. The design was implemented targeting a Spartan3 device (3S200PQ208) then a Virtex II Pro (xc2vp7-6ff672). Obtained results are discussed and compared with an other architecture. The conversion method has been verified successfully with no visually perceptual errors in the transformed images.

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Hardware Co-simulation For Video Processing Using Xilinx System Generator

Various computer systems have attracted more researchers’ attention to arrive at a qualitative diagnosis in a few times. Different brain tumor classification approaches are proposed due to lesion complexity. This complexity makes the early tumor diagnosis using magnetic resonance images (MRI) a hard step. However, the accuracy of these techniques requires a significant amelioration to meet the needs of real-world diagnostic situations. We aim to classify three brain tumor types in this paper. A new technique is suggested which provides excellent results and surpasses the previous schemes. The proposed scheme makes use of the normalization, dense speeded up robust features, and histogram of gradient approaches to ameliorate MRI quality and generate a discriminative feature set. We exploit support vector machine in the classification step. The suggested system is benchmarked on an important dataset. The accuracy achieved based on this scheme is 90.27%. This method surpassed the most recent system according to experimental results. The results were earned through a strict statistical analysis (k-fold cross-validation), which proves the reliability and robustness of the suggested method.

Brain tumor classification based on hybrid approach

Despite the diversity of video compression standard, the motion estimation still remains a key process which is used in most of them. Moreover, the required coding performances (bit-rate, PSNR, image spatial resolution,etc.) depend obviously of the application, the environment and the network communication. The motion estimation can therefore be adapted to fit with these performances. Meanwhile, the real time encoding is required in many applications. To reach this goal, we propose in this paper a flexible hardware implementation of the motion estimator which enables the integer motion search algorithms to be modified and the fractional search as well as variable block size to be selected and adjusted. Hence, this novel architecture, especially designed for FPGA targets, proposes high-speed processing for a configuration which supports the variable size blocks and quarter-pel refinement, as described in H.264. The proposed low-cost architecture based on Virtex 6 FPGA can process integer motion estimation on 1080 HD video streams, respectively, at 13 fps using full search strategy (108k Macroblocks/s) and up to 223 fps using diamond search (1.8M Macroblocks/s). Moreover subpel refinement in quarter-pel mode is performed at 232k Macroblocks/s.

Wajdi Elhamzi

Papers

Deep CNN for Brain Tumor Classification

A hybrid feature extraction approach for brain MRI classification based on Bag-of-words

Hardware Co-simulation For Video Processing Using Xilinx System Generator

Brain tumor classification based on hybrid approach

An efficient low-cost FPGA implementation of a configurable motion estimation for H.264 video coding