Mohamed Abdel-Nasser

Bio: Mohamed Abdel-Nasser is an academic researcher from Aswan University. The author has contributed to research in topics: Computer science & Segmentation. The author has an hindex of 13, co-authored 71 publications receiving 759 citations. Previous affiliations of Mohamed Abdel-Nasser include Rovira i Virgili University & South Valley University.

C13. Region based image registration approach using an artificial immune system

Abstract: In this paper, we proposed a novel image registration approach. This approach consists of three sequential steps : 1) segmentation process using watershed method, 2) point correspondence using an artificial immune system and 3) finally, least mean square minimization technique is used to find the transformation that align the two images. To demonstrate the effectiveness of the proposed approach we compare it with a region based optical flow estimation algorithm.

A computer-aided diagnosis system for breast cancer molecular subtype prediction in mammographic images

Abstract: Several computer-aided diagnosis (CAD) systems have been developed to assist the radiologists for early breast cancer detection and treatment. These CAD systems provide statistical features of a mammogram using computer vision and image processing techniques for characterizing the morphological structure and evolution of the tumors. In this chapter a CAD system is introduced that includes three stages: tumor detection, segmentation, and tumor-shape and molecular subtypes classification based on deep learning models. The first stage is to detect the region of interest (ROI) that contains a tumor from mammographic images by using a modified Faster R-CNN (convolutional neural network) detector, which incorporates an Inception-ResNet-v2 feature extractor with a squeeze and excitation network. While the second stage employs a conditional generative adversarial network (cGAN) to segment the breast tumor from the detected ROI. For shape classification, a CNN is then developed in the third stage of the CAD system to classify the binary masks of the cGAN network into four tumor-shape classes: irregular, lobular, oval, and round. Finally, this chapter presents a study of the correlation between the tumor shapes and molecular subtypes of breast cancer. The findings of this chapter indicate that the tumor shape can be analyzed for understanding the molecular subtype of the tumor.

C16. Multimodal Medical Image Registration Approach Using an Artificial Immune System for Noisy and Partial Data

Abstract: Improvement of medical diagnosis, aided computer surgeries and tumor identification requires an accurate image registration approaches. The registration of multimodal medical images is more complicated than the registration of unimodal medical images due to the variation in luminance between the images. In this paper, an accurate multimodal image registration approach using artificial immune system (AIS) is proposed and the affine transformation model is used in contrast to the most of the related works which assumed rigid transformation model or similarity transformation model. In the proposed approach the LL bands of the discrete wavelet transform (DWT) for the images are used and the normalized mutual information (NMI) is used as a fitness function. The proposed approach achieves good result in the case of noiseless images, noisy images and partial data loss from one of the images. Moreover, the proposed approach does not need any feature extraction or refinement step. To demonstrate the robustness of the proposed approach, it has been compared with two multimodal medical image registration approaches.

Color-Aware Two-Branch DCNN for Efficient Plant Disease Classification

Abstract: Deep convolutional neural networks (DCNNs) have been successfully applied to plant disease detection. Unlike most existing studies, we propose feeding a DCNN CIE Lab instead of RGB color coordinates. We modified an Inception V3 architecture to include one branch specific for achromatic data (L channel) and another branch specific for chromatic data (AB channels). This modification takes advantage of the decoupling of chromatic and achromatic information. Besides, splitting branches reduces the number of trainable parameters and computation load by up to 50% of the original figures using modified layers. We achieved a state-of-the-art classification accuracy of 99.48% on the Plant Village dataset and 76.91% on the Cropped-PlantDoc dataset.

Online Short-term Solar Power Forecasting

Abstract: This paper describes a new approach to online forecasting of power production from PV systems. The method is suited to online forecasting in many applications and in this paper it is used to predict hourly values of solar power for horizons of up to 36 h. The data used is 15-min observations of solar power from 21 PV systems located on rooftops in a small village in Denmark. The suggested method is a two-stage method where first a statistical normalization of the solar power is obtained using a clear sky model. The clear sky model is found using statistical smoothing techniques. Then forecasts of the normalized solar power are calculated using adaptive linear time series models. Both autoregressive (AR) and AR with exogenous input (ARX) models are evaluated, where the latter takes numerical weather predictions (NWPs) as input. The results indicate that for forecasts up to 2 h ahead the most important input is the available observations of solar power, while for longer horizons NWPs are the most important input. A root mean square error improvement of around 35% is achieved by the ARX model compared to a proposed reference model.

Statistical methods in medical research

Abstract: Statistical methods in medical research , Statistical methods in medical research , کتابخانه دیجیتال جندی شاپور اهواز

A comprehensive survey: Whale Optimization Algorithm and its applications

Abstract: Whale Optimization Algorithm (WOA) is an optimization algorithm developed by Mirjalili and Lewis in 2016. An overview of WOA is described in this paper, rooted from the bubble-net hunting strategy, besides an overview of WOA applications that are used to solve optimization problems in various categories. The best solution has been determined to make something as functional and effective as possible through the optimization process by minimizing or maximizing the parameters involved in the problems. Research and engineering attention have been paid to Meta-heuristics for purposes of decision-making given the growing complexity of models and the needs for quick decision making in the engineering. An updated review of research of WOA is provided in this paper for hybridization, improved, and variants. The categories included in the reviews are Engineering, Clustering, Classification, Robot Path, Image Processing, Networks, Task Scheduling, and other engineering applications. According to the reviewed literature, WOA is mostly used in the engineering area to solve optimization problems. Providing an overview and summarizing the review of WOA applications are the aims of this paper.