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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Yazeed Yasin Ghadi
Al Ain University
Computer Science & IT Electrical & Electronics Engineering

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Intelligent computer aided diagnosis system to enhance mass lesions in digitized mammogram images Ayman AbuBaker; Yazeed Yasin Ghadi; Nader Santarisi
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp2564-2570

Abstract

The paper presents an intelligent system to enhance mass lesions in digitized mammogram images. This system can assist radiologists in detecting mass lesions in mammogram images as an early diagnosis of breast cancer. In this paper, the early detection of mass lesion is visually detected by enhancing mass lesions in mammogram images using hybrid neuro-fuzzy technique. Fuzzified engine is proposed as a first step to convert all pixels in mammogram image to a fuzzy value using three linguistic labels. After that, artificial neural networks are used instead of the inference engine to accurately detect the mass lesions in the mammogram images in a short time. Finally, five linguistic labels are used as a defuzzifier engine to restore the mammogram image. Processed mammogram images are extensively evaluated using two different types of mammogram resources, mammographic image analysis society (MIAS) and University of South Florida (USF) databases. The results show that the proposed intelligent computer aided diagnosis system can successfully enhance the mass lesions in mammogram images with minimum number of false positive regions.
Functions of fuzzy logic based controllers used in smart building Ali M. Baniyounes; Yazeed Yasin Ghadi; Eyad Radwan; Khalid S. Al-Olimat
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp3061-3071

Abstract

The main aim of this study is to support design and development processes of advanced fuzzy-logic-based controller for smart buildings e.g., heating, ventilation and air conditioning, heating, ventilation and air conditioning (HVAC) and indoor lighting control systems. Moreover, the proposed methodology can be used to assess systems energy and environmental performances, also compare energy usages of fuzzy control systems with the performances of conventional on/off and proportional integral derivative controller (PID). The main objective and purpose of using fuzzy-logic-based model and control is to precisely control indoor thermal comfort e.g., temperature, humidity, air quality, air velocity, thermal comfort, and energy balance. Moreover, this article present and highlight mathematical models of indoor temperature and humidity transfer matrix, uncertainties of users’ comfort preference set-points and a fuzzy algorithm.
Optimization of fuzzy rules using neural network to control mobile robot in non-structured environment Ayman A. AbuBaker; Yazeed Yasin Ghadi
Bulletin of Electrical Engineering and Informatics Vol 12, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i5.5029

Abstract

It is still a challenge for all authors to control an autonomous mobile robot in an unstructured environment. The purpose of this paper is to propose a new control method for mobile robots in unstructured environments using neuro fuzzy technique. The proposed algorithm reduces the processing time of the fuzzy logic controller (FLC) inference engine. The neural network (NN) will therefore select the optimum rule(s) directly from the inference engine. This means that all of the rules of the inference engine do not need to be processed. As a result, the inference engine process speed will decrease, and the fuzzy logic response will increase. An actual mobile robot with three distance sensors and one virtual orientation angle is used to test the proposed algorithm. Based on the results, the mobile robot is capable of avoiding all obstacles and reaching the target point accurately.
Texture features analysis technique to detect mass lesion in digitized mammogram images Ayman A. AbuBaker; Yazeed Yasin Ghadi
Bulletin of Electrical Engineering and Informatics Vol 12, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i5.4701

Abstract

Mass lesions are one of the breast cancer tumors. Mammogram images are the first screening tool to detect tumors in the women breast, but due to radiologist fatigue, number of false positive (FP) and false negative (FN) rates are increased. The main objective of this paper is to develop an intelligent computer aided diagnosis (CAD) system that can accurately detect mass lesions in digitized mammogram images. The proposed method has three stages. The first stage is a preprocessing stage, where the mass lesion is enhanced using a customized Laplacian filter. Then, multi-statistical filters are implemented to detect a potential mass lesion in the mammogram images. In the final stage, the number detected FP regions are reduced using five texture features. The proposed algorithm is evaluated using 45 mammogram images and the algorithm achieved an accuracy rate of 97% in detecting mass lesion with 83% sensitivity rate and 98% specificity rate.
Co-Authors Ali M. Baniyounes Ayman A. AbuBaker Ayman AbuBaker Eyad Radwan Khalid S. Al-Olimat Nader Santarisi