Journal of

 Background and Aim: The risk of breast cancer increases directly in line with breast density. Therefore, it is important to pay more attention to denser breasts in order to detect abnormalities. The aim of this paper was to design and suggest a quantitative method to categorize breast density in digital mammogram images using fuzzy logic.

 Materials and Methods: This was a crosectional study which resulted in developing a new system. The research population was including patients who undergo mammography in National Cancer Society of Malaysia during 2010 to 2011. Sample included 220 mammogram images which was selected randomly. Data analysis was done using SPSS with Kappa statistics.

 Results: Accuracy level of 92.8% was obtained based on evaluation of the system and there was a strong correlation between the system output and radiologists’ estimation (K=0.87, p=0.0001).

 Conclusion : Results obtained from the suggested system had higher performance than similar systems. Therefore, it could be concluded that the fuzzy logic may be used in this area. In addition, such systems could be helpful for physicians.

Background and Aim: Diagnosis of preeclampsia has an essential role in applying appropriate treatment plan for the patients. The aim of this study was to design an expert system in order to diagnos preeclampsia in order to assist the clinicians.

Materials and Methods: This was a cross-sectional study which resulted in developing a new system. The study population consisted of all patients admitted to three Maternity hospitals affliated to Tehran University of Medical Sciences (TUMS). Sample size included 215 medical records which were randomly selected. The results obtained were compared with the diagnosis from experts by kappa test using SPSS software.

Results: First of all, input parameters fuzzificated and entered into inference engine. Outputs were categorized in two groups as patients and healthy, with the final diagnosis and clinical explanation. The results obtained from system evaluation showed that accuracy, specificity and sensitivity of the system were 98.2 percent, 100 percent and 96.4 percent respectively.

Conclusion: Based on evaluation results, it could be concluded that fuzzy logic is an efficient method for designing of expert systems in the field of obstetrics and gynecology. Also, due to the similarity of the logic used in the proposed system with workflow and medical decision making, it will be accepted by the physicians.

Background and Aim: Bacterial meningitis detection is a complicated problem because of having several components in order to be diagnosed and distinguished from other types of meningitis. Fuzzy logic and neural network, frequently used in expert systems, are able to distinguish such diseases. The purpose of this paper is to compare Fuzzy logic and artificial neural networks for distinguishing bacterial meningitis from other types of meningitis.
Materials and Methods: In this study to detect and distinguish bacterial meningitis from other types of meningitis, in the first step 6 attributes were selected by infectious disease specialists. In the second step, systems were designed by Matlab software. The systems were evaluated by 26 records of meningitis patients, and results were analyzed by SPSS software.
Results: The evaluation showed that the accuracy, specificity and sensitivity of fuzzy method were 88%, 92% and 100% respectively and those of neural network methods were 92%, 94% and 88% respectively. The Kappa test result in fuzzy and neural network methods were 0.83 (p<0.001) and 0.83 (p<0.001). The areas under the ROC curves were 0.94 and 0.91 respectively.
Conclusion: The sensitivity, the Kappa test results and the areas under the ROC curve of the fuzzy logic method were better than neural network method. However the fuzzy logic method is more reliable to distinguish bacterial meningitis from other type of Meningitis, the evaluation result were obtained from 26 records of meningitis patient which were hospitalized in the same center leads to the study be still open.