An Efficient Algorithm on Based GLCM-PNN to Diagnose Malaria
عنوان مقاله: An Efficient Algorithm on Based GLCM-PNN to Diagnose Malaria
شناسه ملی مقاله: ICESCON01_0409
منتشر شده در کنفرانس بین المللی علوم و مهندسی در سال 1394
شناسه ملی مقاله: ICESCON01_0409
منتشر شده در کنفرانس بین المللی علوم و مهندسی در سال 1394
مشخصات نویسندگان مقاله:
Alireza Akhlaghi - M.S. Student, Dept. Computer and Informatics Engineering, Payame Noor UniversityQeshm, Iran
Mehdi Khalili - Assistant Professor, Dept. Computer and Informatics Engineering, Payame Noor University Tehran, Iran
خلاصه مقاله:
Alireza Akhlaghi - M.S. Student, Dept. Computer and Informatics Engineering, Payame Noor UniversityQeshm, Iran
Mehdi Khalili - Assistant Professor, Dept. Computer and Informatics Engineering, Payame Noor University Tehran, Iran
Malaria is a serious infectious disease, and early and accurate diagnosis is necessary in order to keep it under control. In this paper, we propose an efficient algorithm to diagnose malaria using Gray-Level Co-Occurrence Matrix (GLCM) and a probabilistic neural network (PNN). In the proposed algorithm, after pre-processing, the red blood cells were separated from images using an active contour model. Consequently, 44 features were extracted from the images using GLCM. Finally, the features were classified into normal and abnormal groups by PNN.The results show that compared to previous studies, the proposed algorithm led to improved results and accurately assessed 557.99 of 851 hospital records.
کلمات کلیدی: malaria; probabilistic neural network; Gray-Level Co-Occurrence Matrix; active contour models; classification; Feature Extraction
صفحه اختصاصی مقاله و دریافت فایل کامل: https://civilica.com/doc/424545/