Leveraging Disease Progression Learning for Medical Image Recognition

Unlike natural images, medical images often have intrinsic characteristics that can be leveraged for neural network learning. For example, images that belong to different stages of a disease may continuously follow a certain progression pattern. In this paper, we propose a novel method that leverages disease progression learning for medical image recognition. In our method, sequences of images ordered by disease stages are learned by a neural network that consists of a shared vision model for feature extraction and a long short-term memory network for the learning of stage sequences. Auxiliary vision outputs are also included to capture stage features that tend to be discrete along the disease progression. Our proposed method is evaluated on a public diabetic retinopathy dataset, and achieves about 3.3% improvement in disease staging accuracy, compared to the baseline method that does not use disease progression learning

Unmanned Aerial Vehicle (UAV): Flight Performance

It is important that when measuring the sideslip angle and angles of attack during flight test performance of a UAV (Unmanned Aerial Vehicle), to fully understand that the Angles of attack and sideslip are parameters that aid in determining the safety of the flight as they improve stability and control of the aircraft. The disadvantage of the measurement of these angles using this method is low accuracy of measurement due to friction to the potentiometers in connection with the vanes. To counter this, a new sensing method was developed to minimize friction and collect more accurate data. The method is based on a pivoted vane type sensor. Findings from this research will later be used to advance other graduate research projects

Maintaining Path Stability with Node Failure in Mobile Ad Hoc Networks

AbstractAs the demand for mobile ad hoc wireless network (MANET) applications grows, so does their use for many important services where reliability and stability of the communication paths are of great importance. Therefore, a MANET must be able to establish reliable communication channels which are protected by failure recovery protocols. One approach for existing failure recovery protocols is based on using backup paths, or multi-paths. This technique provides for more stable communication channels for wireless services, in particular for MANET applications. But work on such multi-path protocols has focused on stability in the presence of link failure for MANETs. In this paper, we extend such protocols to maintain connection stability in the presence of node failure. Our work is focused on protecting the route of mobile wireless communications in the presence of node failure in order to improve their use in MANETs applications by discovering efficient stable communication channels with longer lifetimes and increased number of packets delivered

Influence of nuclei segmentation on breast cancer malignancy classification

Breast Cancer is one of the most deadly cancers affecting middle–aged women. Accurate diagnosis and prognosis are crucial to reduce the high death rate. Nowadays there are numerous diagnostic tools for breast cancer diagnosis. In this paper we discuss a role of nuclear segmentation from fine needle aspiration biopsy (FNA) slides and its influence on malignancy classification. Classification of malignancy plays a very important role during the diagnosis process of breast cancer. Out of all cancer diagnostic tools, FNA slides provide the most valuable information about the cancer malignancy grade which helps to choose an appropriate treatment. This process involves assessing numerous nuclear features and therefore precise segmentation of nuclei is very important. In this work we compare three powerful segmentation approaches and test their impact on the classification of breast cancer malignancy. The studied approaches involve level set segmentation, fuzzy c–means segmentation and textural segmentation based on co–occurrence matrix. Segmented nuclei were used to extract nuclear features for malignancy classification. For classification purposes four different classifiers were trained and tested with previously extracted features. The compared classifiers are Multilayer Perceptron (MLP), Self–Organizing Maps (SOM), Principal Component–based Neural Network (PCA) and Support Vector Machines (SVM). The presented results show that level set segmentation yields the best results over the three compared approaches and leads to a good feature extraction with a lowest average error rate of 6.51% over four different classifiers. The best performance was recorded for multilayer perceptron with an error rate of 3.07% using fuzzy c–means segmentation

Counting of RBCs and WBCs in noisy normal blood smear microscopic images

This work focuses on the segmentation and counting of peripheral blood smear particles which plays a vital role in medical diagnosis. Our approach profits from some powerful processing techniques. Firstly, the method used for denoising a blood smear image is based on the Bivariate wavelet. Secondly, image edge preservation uses the Kuwahara filter. Thirdly, a new binarization technique is introduced by merging the Otsu and Niblack methods. We have also proposed an efficient step-by-step procedure to determine solid binary objects by merging modified binary, edged images and modified Chan-Vese active contours. The separation of White Blood Cells (WBCs) from Red Blood Cells (RBCs) into two sub-images based on the RBC (blood’s dominant particle) size estimation is a critical step. Using Granulometry, we get an approximation of the RBC size. The proposed separation algorithm is an iterative mechanism which is based on morphological theory, saturation amount and RBC size. A primary aim of this work is to introduce an accurate mechanism for counting blood smear particles. This is accomplished by using the Immersion Watershed algorithm which counts red and white blood cells separately. To evaluate the capability of the proposed framework,experiments were conducted on normal blood smear images. This framework was compared to other published approaches and found to have lower complexity and better performance in its constituent steps; hence, it has a better overall performance

A Parallel Algorithm for Solving the 3d Schrodinger Equation

We describe a parallel algorithm for solving the time-independent 3d Schrodinger equation using the finite difference time domain (FDTD) method. We introduce an optimized parallelization scheme that reduces communication overhead between computational nodes. We demonstrate that the compute time, t, scales inversely with the number of computational nodes as t ~ N_nodes^(-0.95 +/- 0.04). This makes it possible to solve the 3d Schrodinger equation on extremely large spatial lattices using a small computing cluster. In addition, we present a new method for precisely determining the energy eigenvalues and wavefunctions of quantum states based on a symmetry constraint on the FDTD initial condition. Finally, we discuss the usage of multi-resolution techniques in order to speed up convergence on extremely large lattices.Comment: 18 pages, 7 figures; published versio