Gehad Ismail Sayed

Feature selection via a novel chaotic crow search algorithm

Crow search algorithm (CSA) is a new natural inspired algorithm proposed by Askarzadeh in 2016. The main inspiration of CSA came from crow search mechanism for hiding their food. Like most of the optimization algorithms, CSA suffers from low convergence rate and entrapment in local optima. In this paper, a novel meta-heuristic optimizer, namely chaotic crow search algorithm (CCSA), is proposed to overcome these problems. The proposed CCSA is applied to optimize feature selection problem for 20 benchmark datasets. Ten chaotic maps are employed during the optimization process of CSA. The performance of CCSA is compared with other well-known and recent optimization algorithms. Experimental results reveal the capability of CCSA to find an optimal feature subset which maximizes the classification performance and minimizes the number of selected features. Moreover, the results show that CCSA is superior compared to CSA and the other algorithms. In addition, the experiments show that sine chaotic map is the appropriate map to significantly boost the performance of CSA.

Detection of breast abnormalities of thermograms based on a new segmentation method

Breast cancer is one from various diseases that has got great attention in the last decades. This due to the number of women who died because of this disease. Segmentation is always an important step in developing a CAD system. This paper proposed an automatic segmentation method for the Region of Interest (ROI) from breast thermograms. This method is based on the data acquisition protocol parameter (the distance from the patient to the camera) and the image statistics of DMR-IR database. To evaluated the results of this method, an approach for the detection of breast abnormalities of thermograms was also proposed. Statistical and texture features from the segmented ROI were extracted and the SVM with its kernel function was used to detect the normal and abnormal breasts based on these features. The experimental results, using the benchmark database, DMR-IR, shown that the classification accuracy reached (100%). Also, using the measurements of the recall and the precision, the classification results reached 100%. This means that the proposed segmentation method is a promising technique for extracting the ROI of breast thermograms.

Bio-inspired Swarm Techniques for Thermogram Breast Cancer Detection

Bio-inspired swarm techniques are a well-established paradigm with current systems having many of the characteristics of biological computers and capable of performing a variety of tasks that are difficult to do using conventional techniques. These techniques involving the study of collective behavior in decentralized systems. Such systems are made up by a population of simple agents interacting locally with one other and with their environment. The system is initialized with a population of individuals (i.e., potential solutions). These individuals are then manipulated over many iteration steps by mimicking the social behavior of insects or animals, in an effort to find the optima in the problem space. A potential solution simplifies through the search space by modifying itself according to its past experience and its relationship with other individuals in the population and the environment. Problems like finding and storing foods, selecting and picking up materials for future usage require a detailed planning, and are solved by insect colonies without any kind of supervisor or controller. Since 1990, several collective behavior (like social insects, bird flocking) inspired algorithms have been proposed. The objective of this article is to present to the swarms and biomedical engineering research communities some of the state-of-the-art in swarms applications to biomedical engineering and motivate research in new trend-setting directions. In this article, we present four swarms algorithms including Particle swarm optimization (PSO), Grey Wolf Optimizer (GWO), Moth Flame Optimization (MFO), and Firefly Algorithm Optimization (FA) and how these techniques could be successfully employed to tackle segmentation biomedical imaging problem. An application of thermography breast cancer imaging has been chosen and the scheme have been applied to see their ability and accuracy to classify the breast cancer images into two outcomes: normal or non-normal.

Moth-flame swarm optimization with neutrosophic sets for automatic mitosis detection in breast cancer histology images

This paper presents an automatic mitosis detection approach of histopathology slide imaging based on using neutrosophic sets (NS) and moth-flame optimization (MFO). The proposed approach consists of two main phases, namely candidate’s extraction and candidate’s classification phase. At candidate’s extraction phase, Gaussian filter was applied to the histopathological slide image and the enhanced image was mapped into the NS domain. Then, morphological operations have been implemented to the truth subset image for more enhancements and focus on mitosis cells. At candidate’s classification phase, several features based on statistical, shape, texture and energy features were extracted from each candidate. Then, a principle of the meta-heuristic MFO algorithm was adopted to select the best discriminating features of mitosis cells. Finally, the selected features were used to feed the classification and regression tree (CART). A benchmark dataset consists of 50 histopathological images was adopted to evaluate the performance of the proposed approach. The adopted dataset consists of five distinct breast pathology slides. These slides were stained with H&E acquired by Aperio XT scanners with 40-x magnification. The total number of mitoses in 50 database images is 300, which were annotated by an expert pathologist. Experimental results reveal the capability of the MFO feature selection algorithm for finding the optimal feature subset which maximizing the classification performance compared to well-known and other meta-heuristic feature selection algorithms. Also, the high obtained value of accuracy, recall, precision and f-score for the adopted dataset prove the robustness of the proposed mitosis detection and classification approach. It achieved overall 65.42 % f-score, 66.03 % recall, 65.73 % precision and accuracy 92.99 %. The experimental results show that the proposed approach is fast, robust, efficient and coherent. Moreover, it could be used for further early diagnostic suspicion of breast cancer.

A novel chaotic salp swarm algorithm for global optimization and feature selection

Salp Swarm Algorithm (SSA) is one of the most recently proposed algorithms driven by the simulation behavior of salps. However, similar to most of the meta-heuristic algorithms, it suffered from stagnation in local optima and low convergence rate. Recently, chaos theory has been successfully applied to solve these problems. In this paper, a novel hybrid solution based on SSA and chaos theory is proposed. The proposed Chaotic Salp Swarm Algorithm (CSSA) is applied on 14 unimodal and multimodal benchmark optimization problems and 20 benchmark datasets. Ten different chaotic maps are employed to enhance the convergence rate and resulting precision. Simulation results showed that the proposed CSSA is a promising algorithm. Also, the results reveal the capability of CSSA in finding an optimal feature subset, which maximizes the classification accuracy, while minimizing the number of selected features. Moreover, the results showed that logistic chaotic map is the optimal map of the used ten, which can significantly boost the performance of original SSA.

Nature Inspired Optimization Algorithms for CT Liver Segmentation

Nature inspired optimization algorithms have gained popularity in the last two decades due to their efficiency and flexibility when they applied to solve global optimization problems. These algorithms are inspired from the biological behavior by swarms of birds, fish and bees. In this chapter, we give an overview of some of nature inspired optimization algorithms such as Artificial Bee Colony (ABC), Cuckoo Search (CS), Social Spider Optimization (SSO) and Grey Wolf Optimization (GWO). Also, we present the usage of ABC and GWO algorithms for CT liver segmentation. The experimental results of the two selected algorithms show that the two algorithms are powerful and can obtain good results when applied to segment medical images.

A hybrid segmentation approach based on Neutrosophic sets and modified watershed: A case of abdominal CT Liver parenchyma

Liver cancer is one of the most common internal malignancies worldwide and also one of the most leading death causes disease. Early detection and accurate staging of liver cancer is considered an important issue in practical radiology. In this paper, a hybrid segmentation approach based on the modified Watershed algorithm and Neutrosophic logics is proposed for liver segmentation from abdominal CT images. The proposed approach consists of three fundamental phases: (1) preprocessing, (2) CT image transformation to Neutrosophic domain and (3) post-processing phase. At preprocessing phase, histogram equalization and median filter are applied to enhance the contrast and intensity values of the liver CT image as well as removing the noise. The enhanced CT liver image is transformed and represented in the Neutrosophic set domain via three membership sets. Finally, at post-processing phase, mathematical morphology and modified watershed algorithm are used to enhance the obtained truth image produced from the previous phase and to extract liver from CT image. Several measurements are used to evaluate the performance of the proposed segmentation approach. It obtains overall accuracy almost 95%. Moreover, it compared with other approaches and achieves better results.

Quantum multiverse optimization algorithm for optimization problems

In this paper, a new hybrid algorithm called quantum multiverse optimization (QMVO) is proposed. The proposed QMVO is based on quantum computing and multiverse optimization (MVO) algorithm. The main features of quantum theory and MVO were applied in a new algorithm to find the optimal trade-off between exploration and exploitation. QMVO algorithm depends on adopting a quantum representation of the search space and the integration of the quantum interference and operators in the multiverse optimization algorithm to obtain the optimal solution of the objective function. The performance of QMVO algorithm is evaluated by using 50 unimodal and multimodal benchmark functions. The experimental results show that the proposed algorithm has comprehensive superiority in solving complex numerical optimization problems. Also, the results show that the proposed QMVO is a promising optimization algorithm compared with other well-known and popular algorithms.

Interphase cells removal from metaphase chromosome images based on meta-heuristic Grey Wolf Optimizer

Interphase cells are undivided and the condensed mass of chromosomes. They can highly decrease the efficiency of automatic karyotype. karyotype is a test that is used to examine chromosomes. This test includes counting the number of chromosomes and finding the structural changes in chromosomes. Removal of these interphase cells is challenging task, because of color similarity between interphase cells, chromosomes and parts of the background. In this paper, a new fully automatic approach based on Fast Fuzzy C-Means (FFCM) and Grey Wolf Optimization (GWO) has been proposed. The proposed approach is used to remove interphase cells and extract chromosomes from metaphase chromosomes image. It comprised of three phases. These phases are preprocessing phase, chromosomes image clustering phase and post-processing phase. The obtained results show a good performance of the proposed approach. It obtains overall 94% accuracy.

An Automated Computer-aided Diagnosis System for Abdominal CT Liver Images

Abstract In this paper, we present a computer-aided diagnosis (CAD) system for abdominal Computed Tomography liver images that comprises four main phases: liver segmentation, lesion candidate segmentation, feature extraction from each candidate lesion, and liver disease classification. A hybrid approach based on fuzzy clustering and grey wolf optimisation is employed for automatic liver segmentation. Fast fuzzy c-means clustering is used for lesion candidates extraction, and a variety of features are extracted from each candidate. Finally, these features are used in a classification stage using a support vector machine. Experimental results confirm the efficacy of the proposed CAD system, which is shown to yield an overall accuracy of almost 96% in terms of healthy liver extraction and 97% for liver disease classification.