برنامه های غربالگری از ماموگرافی به عنوان ابزار تشخیصی اولیه برای شناسایی زودهنگام سرطان پستان استفاده می کنند. هدف از انجام اين کار، امكان سنجي جداسازي اتوماتيك تصاوير میکروکلسیفیکیشن هاي بافت پستان و همچنين ارزيابي دقت آن با استفاده از به كارگيري دو تكنيك بهبود تصویر و برجسته سازی میکروکلسیفیکیشن هاي بافت سینه برای نواحی مورد نظر ROI به کمک سیستم فازی بر اساس کانتراست منطقه و روش فیلترینگ گابور اشاره شده است. بعد از تعیین خوشه های میکروکلسیفیکیشن هاي بافت پستان، طبقه بندی خوشه ها با کمک الگوریتم طبقه بندی درخت تصمیم انجام مي شود. در ادامه برای بخش بندی نمونه های مشکوک به میکروکلسیفیکیشن برجسته و ماسک گذاری می شود و در مرحله آخر ویژگیهای بافت استخراج شده است و با کمک شبکه عصبی مصنوعی (ANN) برای تعیین نوع خوشخیم و بدخیم خوشه های بخش بندی شده ROI، استفاده می شود. نتایجی این کار انجام شده نشان دهنده دقت بالای 93% و بهبود حساسیت بالای 95% می باشد، که نشان می دهد راهکار ارائه شده می تواند با اطمینان برای تشخیص سرطان پستان اعمال شود. Manuscript profile

According to the report of the World Health Organization, corona disease is the most dangerous and contagious disease in the world. Currently, the most common method used to diagnose corona disease is the polymer chain reaction laboratory technique of reverse transcription, but since this method requires time to confirm the presence of the virus in the laboratory and also due to the unavailability of diagnostic kits and its high costs, Suspected corona virus patients cannot be identified and treated in time; This, in turn, can increase the likelihood of spreading the disease.Another diagnostic method is the use of X-ray chest imaging technique as well as chest computed tomography scan. Also, the use of deep learning methods can be very important for faster and more accurate diagnosis of the lung problems of the corona virus.In this study, using optimized deep convolutional networks based on X-ray images, patients with corona virus were diagnosed.In this article, using the optimized convolutional neural network of healthy people and those with corona, with 10-Fold cross-validation, average accuracy of 98.9% and average sensitivity of 96.5% were obtained.According to the obtained results, it can be said that the proposed method has the ability to separate healthy and unhealthy signals with acceptable accuracy. Manuscript profile

According to the information of the World Health Organization, today heart diseases are considered the most important threat to humans and are the first cause of death in the world. According to the latest global statistics, 46% of deaths are related to the heart. According to reports and research, a large number of causes of death are caused by heart diseases, while 25% of cases are reversible. Correct and timely diagnosis of patients with acute heart problems can largely prevent sudden death and further problems.Due to the fact that recording an electrocardiogram is inexpensive and fruitful, the use of an electrocardiogram can help a lot in many heart diseases and other diseases.Deep learning is one of the new methods with high accuracy in diagnosis and classification, which is based on the convolutional neural network.Convolutional neural networks have a very high processing and training time, which can be optimized and reduced in order to reduce the time, so that acceptable results can be obtained with high accuracy.In this article, using the optimized convolutional neural network, the healthy and unhealthy signal was obtained with 99.9% accuracy and 99.7% sensitivity with 10-fold cross-validation.According to the obtained results, it can be said that the proposed method has the ability to separate healthy and unhealthy signals with acceptable accuracy. Manuscript profile

Retinal blood vessels include arteries and veins and are usually next to each other. Blood vessels are used to classify the severity of the disease and are also used for guidance during surgery, as retinopathy is one of the dangerous diseases.Diabetic retinopathy can cause the formation of new vessels (neoangiogenesis). This condition causes low vision and even blindness. Therefore, a reliable method for diagnosing and classifying the vessel is needed in order to avoid these complications. Retinopathy is one of the hidden diseases that is usually not known. prevent the next possibility.There are several methods for diagnosis, the most common of which is the use of traditional methods based on manual feature extraction, which requires a lot of feature geometry and expertise, and is usually dependent on data.From this method, neural convolution is a reliable, efficient and reliable method for extracting features without manual intervention, which requires a lot of expertise, which also reduces the dependence on data.In this article, using convolutional neural network, diabetic retinopathy has been diagnosed with accuracy and sensitivity of 98.8% and 97.5%, respectively.The obtained results indicate that the proposed method is suitable for locating blood vessels automatically. Manuscript profile