Tehran University Medical Journal

Background: One common symptom of diabetes is diabetic retinopathy, if not timely diagnosed and treated, leads to blindness. Retinal image analysis has been currently adopted to diagnose retinopathy. In this study, a model of hierarchical self-organized neural networks has been presented for the detection and classification of retina in diabetic patients.
Methods: This study is a retrospective cross-sectional, conducted from December to February 2015 at the AJA University of Medical Sciences, Tehran. The study has been conducted on the MESSIDOR base, which included 1200 images from the posterior pole of the eye. Retinal images are classified into 3 categories: mild, moderate and severe. A system consisting of a new hybrid classification of SOM has been presented for the detection of retina lesions. The proposed system includes rapid preprocessing, extraction of lesions features, and finally provision of a classification model. In the preprocessing, the system is composed of three processes of primary separation of target lesions, separation of the optical disk, and separation of blood vessels from the retina. The second step is a collection of features based on various descriptions, such as morphology, color, light intensity, and moments. The classification includes a model of hierarchical self-organized networks named HSOM which is proposed to accelerate and increase the accuracy of lesions classification considering the high volume of information in the feature extraction.
Results: The sensitivity, specificity and accuracy of the proposed model for the classification of diabetic retinopathy lesions is 98.9%, 96.77%, 97.87%, respectively.
Conclusion: These days, the cases of diabetes with hypertension are constantly increasing, and one of the main adverse effects of this disease is related to eyes. In this respect, the diagnosis of retinopathy, which is the same as identification of exudates, microanurysm and bleeding, is of particular importance. The results show that the proposed model is able to detect lesions in diabetic retinopathy images and classify them with an acceptable accuracy. In addition, the results suggest that this method has an acceptable performance compared to other methods.

Background: Breast cancer is a common malignancy in which early breast cancer detection by the help of imaging can improve the treatment outcome. Thermography utilizes infrared beams which are fast, non-invasive, and non-contact and the output created images by this technique are flexible and useful to monitor the temperature of the human body.
Case presentation: Our patient is a 25-year-old woman who was referred to Tehran's Imam Khomeini Hospital, Tehran University of Medical Sciences, in October 2014 and June 2017 to perform clinical examinations of breast cancer at the Invasive and New Radiology Research Center of Tehran. The results of the sonography for the left breast and bilateral axillary regions and sonography guided biopsy from the left axillary region indicated that:  it was consistent with the tangential prominence at 11-12 O’ clock in the left breast tissue and echo gene was found without any suspected findings. Then, using the non-contact infrared imaging camera VisIR 640 (Thermoteknix Systems Ltd, Cambridge, UK), the feasibility of thermography method in the patient's follow-up was investigated.
Conclusion: Thermography can be used to detect abnormal areas in the breast tissue that may have cystic origin. The results indicated that the accuracy of the identification and matching of patient cysts in mammography and ultrasonography with the results of thermography in both periods of October 2014 and June 2017. Considering the results, it is noteworthy that the diagnostic clock of the breast cysts in the patient is consistent with the results of the clinical trials with the thermography. Moreover, in a 2 years intervals, the status of thermal morphology status of the cystic region did not considerably change which showed a relatively stable status.