در این مقاله، تشخیص تومور مغز از طریق به کارگیری تکنیک‌های پیشرفته یادگیری عمیق مورد بررسی قرار گرفته است. رویکرد این مطالعه شامل توسعه و آموزش یک معماری جامع از شبکه عصبی کانولوشنی (CNN) با بهره‌گیری از یک مجموعه داده گسترده از تصاویر رزونانس مغناطیسی مغز (MRI) می¬باشد، مدل پیشنهادی در طبقه‌بندی بافت معمولی مغز و مناطق تحت تأثیر تومور بسیار توانمند است. این معماری شامل لایه‌های متعدد از جمله لایه‌های کانولوشنی، نرمال‌سازی دسته‌ای و لایه‌های پولینگ است که در نهایت به یک لایه قوی طبقه‌بندی منجر می‌شود. از طریق آموزش دقیق و بهینه‌سازی، شبکه عصبی کانولوشنی معرفی‌شده توانسته است در طبقه‌بندی تومور مغز به دقت بالایی دست یابد. اثربخشی این مدل پیشنهادی از طریق آزمایشات جامع به نمایش گذاشته شده که نشان‌دهنده قابلیت آن در تشخیص دقیق تومور مغز است. پرونده مقاله

Epilepsy is a chronic disorder and outbreak of brain function, caused by the abnormal and intermittent electric discharge of brain neurons. Electroencephalogram signals represent brain activities, and one of the methods of diagnosing epilepsy is using EEG brain signals. In this article, a new method for diagnosing epilepsy using EEG signal processing is presented. At first, the EEG signal is divided into five frequency sub-bands using Discrete Wavelet Transformation (DWT). Then, the features are extracted from five frequency sub-bands, and the best features are selected by the analysis of variance (ANOVA) method. Finally, by using the Support Vector Machine (SVM) algorithm, these features are used to classify seizure and non-seizure EEG signals. The simulation results from the Bonn university dataset affirm the suggested approach's advantage in comparison with some other basic classical methods in terms of accuracy, sensitivity, and specificit. پرونده مقاله

Listening to the sound signals of the heart is considered one of the most non-invasive and easy ways to diagnose the irregularities of the human heart, the correct analysis of which requires the knowledge and experience of a specialist doctor. The purpose of this paper is to design and implement a computer-aided diagnosis (CAD) system for detecting and classifying normal and abnormal heart sounds from phonocardiogram (PCG) signals. To perform experiments, the PhysioNet database was used. In the pre-processing step, noise and environmental disturbances in the PCG signals are removed using band-pass Butterworth filters. Then, discrete wavelet transforms (DWT), group-based Sparse, and tensor decomposition are used to extract features from PCG signals. Finally, the support vector machine (SVM), the k-nearest neighbors (KNN), naive Bayes (NB), the classification and regression tree (CART), and multi-layer perceptron (MLP) were used for the classification step. The employment of DWT, group-based sparse, and tensor decomposition for detection features is the novelty of this paper. The proposed method demonstrated better performance compared to other methods used in different papers. The proposed DWT, group-based sparse and tensor decomposition-NB method had a high accuracy rate of 95.3%. Also, the combination of PCG feature extraction methods increases the accuracy of the CAD system in diagnosing abnormal heart sounds. The proposed method in this paper uses different methods for extracting features, and their classification has high accuracy for abnormal sound detection. پرونده مقاله