Classification Of Breast Lesions Using Artificial Neural Network.

This paper presents a study on classification of breast lesions using artificial neural network. Thirteen morphological features have been extracted from breast lesion cells and used as the neural network inputs for the classification

On-Line Modelling And Forecasting Of Carbon Monoxide Concentrations Using Hybird Multilayered Perceptron Network.

This paper discusses on-line modelling and forecasting of carbon monoxide (CO) concentrations using Hybrid Multilayered Perceptron (HMLP) Network. Th¢ HMLP network is trained using Modified Recursive Prediction Error (MRPE) algorithm

HMLP,MLP and Recurrent Networks for Carbon Monoxide Concentrations Forecasting: A Comparison Studies.

Carbon Monoxide (CO) is a primary pollutant in urban area, due to the major emission from motor vehicles. Forecasting of CO or other gas pollutants concentration are very important since preventive action can be taken if the forecasted CO level exceeds certain value

Nano-satellite attitude control system based on adaptive neuro-controller

The current research focuses on designing of an intelligent controller for attitude control system (ACS) of nano-satellite. The nanosatellite namely Innovative Satellite (InnoSAT) was organized by Agensi Angkasa Negara (ANGKASA) to attract the interest of Malaysian universities in satellite development.In this study, an intelligent controller based on Hybrid Multi Layered Perceptron (HMLP) network was developed. The network used model reference adaptive control (MRAC) system as a control scheme to control a time varying systems where the performance specifications are given in terms of a reference model.The Weighted Recursive Least Square (WRLS) algorithm will adjust the controller parameters to minimize error between the plant output and the model reference output.The objective of this paper is to analyze the tracking performance of ANC based on HMLP network and ANC based on standard MLP network for controlling a satellite attitude. The simulation results indicate that ANC based on HMLP network gave better performance than ANC based on standard MLP network

Automated classification of blasts in acute leukemia blood samples using HMLP network

This paper presents a study on classification of blasts in acute leukemia blood samples using artificial neural network.In acute leukemia there are two major forms that are acute myelogenous leukemia (AML) and acute lymphocytic leukemia (ALL).Six morphological features have been extracted from acute leukemia blood images and used as neural network inputs for the classification.Hybrid Multilayer Perceptron (HMLP) neural network was used to perform the classification task.The Hybrid Multilayer Perceptron(HMLP) neural network is trained using modified RPE(MRPE) training algorithm for 1474 data samples.The Hybrid Multilayer Perceptron (HMLP) neural network produces 97.04% performance accuracy.The result indicates the promising capabilities and abilities of the Hybrid Multilayer Perceptron (HMLP) neural network using modified RPE (MRPE) training algorithm for classifying and distinguishing the blasts from acute leukemia blood samples

Performance Comparison of Segmentation Techniques for Nucleus in Chronics Leukemia

Morphological criteria have been used by haematologists to identify malignant cells in the blood smear sample under a light microscope. Experienced hematologist must perform this screening operation. However, manual screening using microscope is time-consuming and tedious. Thus, an automated or semi-automated image screening and diagnosis system are very helpful. An ideal automated screening system will acts as a human expert during the procedure. To formulate this idea, there are few steps involves in this process which is the acquisition of image, image segmentation, features extraction and recognition of image data for further analysis in computer-based. However, segmentation of a region of interest is the most crucial task to extract features for further learning and diagnose. This paper represents two segmentation techniques and their performance comparison based on clustering approach which are k-means and moving k-means clustering algorithms. The segmentation process is performed on ten chronics leukaemia images. The performance of segmentation based on the proposed techniques was evaluated. The proposed segmentation techniques offer high accuracies of segmentation which is more than 97% for both techniques

Implementation of high dynamic range rendering on acute leukemia slide images using contrast stretching

Acute leukemia is one of the critical disease that requires immediate treatment due to the rapid progression and accumulation of the cancerous cells. In recent years, image processing techniques had been explored to enhance the diagnosis of acute leukemia. However, microscopic image captured from the light microscope usually has poor quality due to the capability of the camera and improper operation by human operator. High Dynamic Range (HDR) imaging technique has been explored to solve the problem by increasing the dynamic range of the images captured. This paper presents a HDR rendering technique by using contrast stretching technique to enhance the morphological features of blast cells. The technique called Partial contrast stretching had been used to render HDR image. The results showed that the proposed method had enhanced the overall contrast and morphological features of the blast cells in the acute leukemia slide images

Image segmentation for Acute Leukemia Cells using color thresholding and median filter

Acute leukemia is a kind of the malignant disease which may lead to death due to its characteristic of rapid development of immature blood cells. Recently, several image processing techniques have been implemented to assist the task of acute leukemia diagnosis. The segmentation of acute leukemia cells is an important key to determine the accuracy of its classification task. This paper proposed a combined technique of color thresholding based on the RGB color information from acute leukemia slide images and median filter to segment the leukemia cells from the unwanted regions such as background and red blood cells. The presented results proved that the proposed technique was successfully segmented the acute leukemia cells from the Acute Myeloid Leukemia and Acute Lymphocytic Leukemia slide images, with the average accuracy rate of 97.63% and 97.64% respectively. Therefore, the proposed image segmentation technique could benefits the classification process of acute leukemia

An Intelligent Recognition Procedure for Trophozoite Stages of Plasmodium Knowlesi Malaria

Plasmodium (P.) Knowlesi is a fifth species of the malaria parasite in the world that caused a serious health problem. Current information suggests that P. Knowlesi is spread to people when an Anopheles mosquito infected by a monkey then bites and infects human (zoonotic transmission). Early identification of P. Knowlesi Malaria is an important step to an effective treatment. P. Knowlesi Malaria identification process is usually carried out with a 100x magnification of thin blood smear using microscope observation. However, this process is time-consuming and very tedious and strenuous for the human eyes. It also has a problem to differentiate between trophozoite, positive control (schizont and gametocyte) and negative control (white blood cell (WBC) and artefact). To overcome these situations, a computer-aided diagnosis system is developed to automatically identifying trophozoite stages of P. Knowlesi Malaria as early identification species, positive control and negative control. The processes involved starting from image acquisition, image processing and recognition. For image processing method, the most important part is the segmentation where the Otsu’s method is utilised to obtain the region of interest (ROI) of the infected cell. The features consist of the size of infected cell and size of the object. Finally, the recognition method using Multilayer Perceptron (MLP) is applied. The results show that the proposed automatic procedure is capable of recognising the trophozoite stage of P. Knowlesi Malaria with an accuracy of 98.70% for training and 97.67% for testing, using MLP trained by Lavernberg Marquardt (LM)