Detection of Leucocytes in Microscopic Images with Swarm Intelligence Algorithm (original) (raw)
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International Journal of Computational Intelligence Systems, 2020
Cancer is a fatal disease that is continuously growing in the developed countries. It is also considered as a main global human health problem. Based on several studies, which have been conducted so far, we found out that Hybrid Particle Swarm Optimization and Artificial Bee Colony Algorithm has never been used in any relevant study; so, in this study we purposed using this algorithm for detecting the centers of the nuclei with the help of histological images to obtain accurate results. If we compare this algorithm with previously proposed algorithms, this algorithm doesn't require a lot of parameters, and besides, it is faster, simpler, and more flexible. This study has been carried out on histological images obtained from a database containing 810 microscopic slides of stained H&E samples from PSB-2015 crowd-sourced nuclei dataset. During the determination process, the noise on images was first eliminated using morphological techniques, and then, we used Hybrid PSO-ABC algorithm to for segmentation of the nucleic images and compared the results with other optimization algorithms to test its accuracy and efficiency. The average 99.38% accuracy rate was assured for cancer nuclei. To demonstrate the robustness of this experiment, the results were compared with other known cancer nuclei detection procedures, which are already mentioned in the literature. Using the new proposed algorithm showed the highest accuracy when it was compared to rest of the methods. The high value outcome confirms that the suggested method outperformed as compared to other algorithms because it shows a higher distinctive ability.
Automatic Leukemia Detection Using Image Processing Technique
This paper is about the proposal of automated leukemia detection approach. In a manual method trained physician count WBC to detect leukemia from the images taken from the microscope. This manual counting process is time taking and not that much accurate because it completely depends on the physician’s skill. To overcome these drawbacks an automated technique of detecting leukemia is developed. This technique involves some filtering techniques and k-mean clustering approach for image preprocessing and segmentation purpose respectively. After that an automated counting algorithm is used to count WBC to detect leukemia. Some features like area, perimeter, mean, centroid, solidity, smoothness, skewness, energy, entropy, homogeneity, standard deviation etc. are extracted and calculated. After that neural network methodology is used to know directly whether the image has cancer effected cell or not. This proposed method has achieved an accuracy of 90%.
Ant Colony optimization algorithm for breast cancer cells classification
2013 International Conference on Electrical Engineering and Software Applications, 2013
Ant colony optimization (ACO) is a bio-inspired technique formalized into a meta-heuristic for combinatorial optimization problems. In this work, the ACO-Otsu segmentation method, based on ACO algorithm and Otsu's method as a fitness function, is applied in classification and detection of breast cancer cells. Subsequently, this method is compared with the Otsu's standard method. The experiments show the performance of this probabilistic search approach in such type of problems.
Integration of Swarm Intelligence and Artificial Neural Network for Medical Image Recognition
2013
Neural network technology plays an important role in the development of new medical diagnostic assistance or what is known as “computer a ided” that based on image recognition.Thispaper study the method used integration of back propagation neural network and Particle Swarm Optimizing (PSO) in parts of recognition the XRay of lungs for two disease cases (cancer and TB) along with the normal case. The experiments show that the improvement of algorithms for recognition side has achieved a good result reached to 88.398% for input image size 1024 pixel and 500 population size. The efficiency and recognition testes for training method was performed and reported in this paper.
Acute leukemia classification by ensemble particle swarm model selection
Artificial intelligence in medicine, 2012
Acute leukemia is a malignant disease that affects a large proportion of the world population. Different types and subtypes of acute leukemia require different treatments. In order to assign the correct treatment, a physician must identify the leukemia type or subtype. Advanced and precise methods are available for identifying leukemia types, but they are very expensive and not available in most hospitals in developing countries. Thus, alternative methods have been proposed. An option explored in this paper is based on the morphological properties of bone marrow images, where features are extracted from medical images and standard machine learning techniques are used to build leukemia type classifiers. This paper studies the use of ensemble particle swarm model selection (EPSMS), which is an automated tool for the selection of classification models, in the context of acute leukemia classification. EPSMS is the application of particle swarm optimization to the exploration of the sear...
A Leukocyte Detection Technique in Blood Smear Images Using Plant Growth Simulation Algorithm
2017
For quite some time, the analysis of leukocyte images has drawn significant attention from the fields of medicine and computer vision alike where various techniques have been used to automate the manual analysis and classification of such images. Analysing such samples manually for detecting leukocytes is time-consuming and prone to error as the cells have different morphological features. Therefore, in order to automate and optimize the process, the nature-inspired Plant Growth Simulation Algorithm (PGSA) has been applied in this paper. An automated detection technique of white blood cells embedded in obscured, stained and smeared images of blood samples has been presented in this paper which is based on a random bionic algorithm and makes use of a fitness function that measures the similarity of the generated candidate solution to an actual leukocyte. As the proposed algorithm proceeds the set of candidate solutions evolves, guaranteeing their fit with the actual leukocytes outlin...
A Swarm-based Approach to Medical Image Analysis
2011
Image segmentation is an indispensable part of the visualization of human tissues, particularly during analysis of Magnetic Resonance (MR) images. Unfortunately images always contain a significant amount of noise due to operator performance, equipment, and the environment can lead to serious inaccuracies with segmentation. A segmentation technique based on an extension to the traditional C-means (FCM) clustering algorithm is proposed in this paper. A neighborhood attraction, which is dependent on the relative location and features of neighboring pixels considered.. The degree of attraction is optimized by a Particle Swarm Optimization model. Paper demonstrates the superiority of the proposed technique to FCM-based method. This segmentation method is component of an MR image-based classification system for tumors, currently being developed.
IOP Conference Series: Materials Science and Engineering
One of the damaging diseases among people in the world is skin cancer. Skin cancer leftovers an important scientific, clinical and public task. Swarm intelligence techniques (SITs) are new, improved and modern methods for optimization algorithms. Failure of detection in skin cancer images can be seen in SITs. This work proposes an efficient image and examines for some samples in this disease. The study presents a simple technique for a pre-processing and an automatic detection of SITs to make the needed analysis. This paper estimated all these various models using the PH 2 , Dermis, ISIC (2016 ,2017, 2018) segmentation challenge dataset. The input images are improved for better processing than, the lesion sampling is segmented from the improved image by using Otsu thresholding and median filter operations. In the earlier studies, skin cancer is analyzed by means of several optimization algorithms. Now, the outcomes of the above algorithms were compared with the dice coefficient and it was demonstrated that the value of 97.35% which is nearer to manual segmentation. The accuracy the value of 98.58% when used for solving the same problem. To this end, a somewhat comprehensive analysis was showed to compare the effectiveness of many parameters' combinations.
Automatic Early Detection and Classification of Leukemia from Microscopic Blood Image
2021
Leukemia is a form of blood cancer that affects white blood cells, and is one of the leading causes of death among humans. Currently, diagnosis of leukemia is done through visual inspection of microscopic images of blood cell, which is time consuming, tedious, and requires trained human experts. Therefore, the lack of an automatic, early, and effective leukemia detection system is a great challenge in Ethiopian hospitals. The main objective of this research is to develop an automatic early detection, and classification system to diagnose leukemia from blood image using machine learning and image processing algorithm. To do the research, 400 leukemic blood images and 50 normal blood images had acquired from Jimma University Specialized Hospital using digital microscope, and preprocessed with contrast enhancement. K-means image segmentation and feature extraction were applied by the system. Multi Class Support Vector Machine has used to provide detection and classification of leukemia...