A brain stroke is a life-threatening medical disorder caused by the inadequate blood supply to the brain. After the stroke, the damaged area of the brain will not operate normally. As a result, early detection is crucial for more effective therapy. Computed tomography (CT) images supply a rapid diagnosis of brain stroke. However, while doctors are analyzing each brain CT image, time is running fast. This circumstance may lead to result in a delay in treatment and making errors. Therefore, we targeted the utilization of an efficient artificial intelligence algorithm in stroke detection. In this paper, we designed hybrid algorithms that include a new convolution neural networks (CNN) architecture called OzNet and various machine learning algorithms for binary classification of real brain stroke CT images. When we classified the dataset with OzNet, we acquired successful performance. However, for this target, we combined it with a minimum Redundancy Maximum Relevance (mRMR) method and Decision Tree (DT), k-Nearest Neighbors (kNN), Linear Discriminant Analysis (LDA), Naïve Bayes (NB), and Support Vector Machines (SVM). In addition, 4096 significant features were obtained from the fully connected layer of OzNet, and we reduced the dimension of features from 4096 to 250 using the mRMR method. Finally, we utilized these machine learning algorithms to classify important features. As a result, OzNet-mRMR-NB was an excellent hybrid algorithm and achieved an accuracy of 98.42% and AUC of 0.99 to detect stroke from brain CT images.

This study develops an automatic algorithm for detecting Alzheimer's disease (AD) using magnetic resonance imaging (MRI) through deep learning and feature selection techniques. It utilizes a dataset of 6400 MRI images from Kaggle, categorized into four classes. Initially, the study employs pretrained CNN architectures—DenseNet-201, MobileNet-v2, ResNet-18, ResNet-50, ResNet-101, and ShuffleNet—for classification using five fold cross-validation, with DenseNet-201 achieving the highest accuracy of 82.11%. Due to the dataset's size and imbalance, as well as the long training times, the study aims to create a more efficient algorithm. The CNNs are used as deep feature extractors from AD images, and the extracted features are reduced using a new fine-tuning neighborhood component analysis (FTNCA) algorithm, which minimizes loss and determines the optimal tolerance value. The essential features are then classified using various machine learning algorithms, including artificial neural network (ANN), K-nearest neighbor (KNN), Naïve Bayes, and support vector machine (SVM). Experimental results reveal that reducing the feature set from 2048 to 344 allows the ResNet-50-FTNCA-KNN model to achieve 100% accuracy, significantly enhancing AD detection. This approach will aid in the early diagnosis and treatment of AD patients.

Monkeypox has recently become an endemic disease that threatens the whole world. The most distinctive feature of this disease is occurring skin lesions. However, in other types of diseases such as chickenpox, measles, and smallpox skin lesions can also be seen. The main aim of this study was to quickly detect monkeypox disease from others through deep learning approaches based on skin images. In this study, MobileNetv2 was used to determine in images whether it was monkeypox or non-monkeypox. To find splitting methods and optimization methods, a comprehensive analysis was performed. The splitting methods included training and testing (70:30 and 80:20) and 10 fold cross validation. The optimization methods as adaptive moment estimation (adam), root mean square propagation (rmsprop), and stochastic gradient descent momentum (sgdm) were used. Then, MobileNetv2 was tasked as a deep feature extractor and features were obtained from the global pooling layer. The Chi-Square feature selection method was used to reduce feature dimensions. Finally, selected features were classified using the Support Vector Machine (SVM) with different kernel functions. In this study, 10 fold cross validation and adam were seen as the best splitting and optimization methods, respectively, with an accuracy of 98.59%. Then, significant features were selected via the Chi-Square method and while classifying 500 features with SVM, an accuracy of 99.69% was observed.