Brain tumor classification using deep convolutional autoencoder-based neural network: multi-task approach

Diagnosis, detection and classification of tumors, in the brain MRI images, are important because misdiagnosis can lead to death. This paper proposes a method that can diagnose brain tumors in the MRI images and classify them into 5 categories using a Convolutional Neural Network (CNN). The proposed network uses a Convolutional Auto-Encoder Neural Network (CANN) to extract and learn deep features of input images. Extracted deep features from each level are combined to make desirable features and improve results. To classify brain tumor into three categories (Meningioma, Glioma, and Pituitary) the proposed method was applied on Cheng dataset and has reached a considerable performance accuracy of 99.3%. To diagnosis and grading Glioma tumors, the proposed method was applied on IXI and BraTS 2017 datasets, and to classify brain images into six classes including Meningioma, Pituitary, Astrocytoma, High-Grade Glioma, Low-Grade Glioma and Normal images (No tumor), the all datasets including IXI, BraTS2017, Cheng and Hazrat-e-Rassol, was used by the proposed network, and it has reached desirable performance accuracy of 99.1% and 98.5%, respectively.

     

Spatial-temporal dual-actor CNN for human interaction prediction in video

Multimedia Tools and Applications - Predicting the interaction between two humans, when viewed as a part of video is one of the most challenging issues in the field of computer vision, due to its...     

CapsNet-based brain tumor segmentation in multimodal MRI images using inhomogeneous voxels in Del vector domain

Multimedia Tools and Applications - Glioma is a type of brain tumor that is the most typical and most aggressive tumor. Magnetic resonance imaging (MRI) has a widespread utilization as an imaging...     

Fast and robust seam estimation to seamless image stitching

Image stitching has a wide range of applications in computer vision/graphics and virtual reality. Seam estimation is one of the key steps in image stitching. This step can relieve ghosts and artifacts that were generated by misalignment or moving objects in the overlap region. This paper presents a fast and robust seam estimation method (FARSE) by defining gray-weighted distance and gradient-domain region of differences to avoid visible seams and ghosting. The optimal seam is estimated by searching in two weighted matrices, namely cost matrix and value matrix. The proposed method could be simply implemented. Results indicate that the FARSE method is scale-invariant and it is fast and more robust than the other methods.     

A non-monotone pattern search approach for systems of nonlinear equations

In this paper, a new pattern search is proposed to solve the systems of nonlinear equations. We introduce a new non-monotone strategy which includes a convex combination of the maximum function of some preceding successful iterates and the current function. First, we produce a stronger non-monotone strategy in relation to the generated strategy by Gasparo et al. [Nonmonotone algorithms for pattern search methods, Numer. Algorithms 28 (2001), pp. 171–186] whenever iterates are far away from the optimizer. Second, when iterates are near the optimizer, we produce a weaker non-monotone strategy with respect to the generated strategy by Ahookhosh and Amini [An efficient nonmonotone trust-region method for unconstrained optimization, Numer. Algorithms 59 (2012), pp. 523–540]. Third, whenever iterates are neither near the optimizer nor far away from it, we produce a medium non-monotone strategy which will be laid between the generated strategy by Gasparo et al. [Nonmonotone algorithms for pattern search methods, Numer. Algorithms 28 (2001), pp. 171–186] and Ahookhosh and Amini [An efficient nonmonotone trust-region method for unconstrained optimization, Numer. Algorithms 59 (2012), pp. 523–540]. Reported are numerical results of the proposed algorithm for which the global convergence is established.