طريقة تصنيف محسنة للكشف عن الامراض في عينات دم الانسان == Improved Classification Approach to Detect Diseases in Human Blood Samples
Author name:
رنا علي سالم
Supervisor name:
جميلة حربي سعود العامري
General topic:
Computer Science
Specific topic:
Computer Science
Degree:
Master
University:
Mustansiriyah University - College Of Science - Department Of Computer
Language:
English
University location:
Baghdad
First pages:
28T837 - p.pdf
Abstract:
Image processing technique for diagnosing diseases in medical image isconsidered very important for human life. Image classification of objectsinto a number of categories or classes is the goal of pattern recognition.Depending on the application, these objects can be images or signalwaveforms or any type of measurements that need to be classified.Microscopic images are allowed to count the classification of bloodcells which is used in evaluating and diagnosis of many diseases. Leukemiais a blood cancer that can be detected through the analysis of WBCs orleukocytes.This thesis aims to improve a classification system to process the inputmicroscope images taken for blood sample, extract the discriminatingfeatures of the White Blood cells (WBCs), and then utilize these featuresto distinguish and recognize the type of cell Leukemia or normal cell. Also,this thesis proposed a system of recognition algorithm, which discriminatesthe WBCs normal or blast cells.The proposed Acute Lymphocytic Leukemia detection andclassification (ALLDC) system for detecting and classifying ALL cells inALL - IDB1 image datasets is used in this thesis. To achieve this aim, ourproposed ALLDC system classifies all cells as ALL and noncancerouscells using two classification techniques applied separately to classify theWBCs normal or blast cells : two classifiers are suggested in our work suchas k - nearest neighbor (KNN) and Artificial Neural Networks (ANN); toclassify WBCs cells has four main steps; The first step is imagepreprocessing, image enhancement is used as preprocessing on this thesis,and that is for improving the quality of images. Nucleus segmentation isthe second step of this thesis. Segmentation of nuclei is performed by usingOtsu’s method frequently applied to segment the image. After applyingsegmentation algorithm on our images, features of nuclei are extractedfrom the result of segmentation part and because there are a high numberof features, some of them are selected as the best features. Featureextraction is considered as the third step, features extracted from nucleiincluding area, perimeter, and circularity are used in KNN classifier andarea, perimeter, circularity, form factor, and minor/major axis are used inANN classifier. The final step is the classification of cells for classificationpart.Classifications rate of defect WBCs is (66.67%), this percentage isimproved by using ANN classifier, where Classification rate of defect cellsis reached (72.22%).
