Dental caries is the most frequent dental health issue in the general population. Dental caries can result in extreme pain or infections, lowering people’s quality of life. Applying machine learning models to automatically identify dental caries can lead to earlier treatment. However, physicians frequently find the model results unsatisfactory due to a lack of explainability. Our study attempts to address this issue with an explainable deep learning model for detecting dental caries. We tested three prominent pre-trained models, EfficientNet-B0, DenseNet-121, and ResNet-50, to determine which is best for the caries detection task. These models take panoramic images as the input, producing a caries–non-caries classification result and a heat map, which visualizes areas of interest on the tooth. The model performance was evaluated using whole panoramic images of 562 subjects. All three models produced remarkably similar results. However, the ResNet-50 model exhibited a slightly better performance when compared to EfficientNet-B0 and DenseNet-121. This model obtained an accuracy of 92.00%, a sensitivity of 87.33%, and an F1-score of 91.61%. Visual inspection showed us that the heat maps were also located in the areas with caries. The proposed explainable deep learning model diagnosed dental caries with high accuracy and reliability. The heat maps help to explain the classification results by indicating a region of suspected caries on the teeth. Dentists could use these heat maps to validate the classification results and reduce misclassification.

Machine learning has been densely used in most computer-aided medical diagnosis systems. These systems not only supported the physician’s decision but also accelerate the necessitated procedures. Electroencephalography (EEG) is an essential device for measuring the brain’s electrical activities. EEG is used to detect a series of brain disorders such as epilepsy, dementia, Parkinson’s disease, and Schizophrenia (SZ). In this work, a novel method for detecting SZ using EEG recordings is suggested. Initially, the presented technique breaks down each channel of the input EEG recordings into EEG rhythms. The wavelet transform is employed to achieve this. The 1D local binary pattern (LBP) is then used to code the acquired rhythm signals. Each row of the input picture is formed by concatenating the uniform histograms of the 1D LBP coded beats. The rows of the images are formed from the channels of the input EEG signal, while the columns of the images are constructed from the rhythms. Extreme learning machines (ELM) based autoencoders (AE) are utilized at a data augmentation step. After data augmentation, the SZ and healthy cases are classified using well-known deep transfer learning. Deep transfer learning employs a variety of pre-trained deep Convolutional Neural Network (CNN) models. Various performance assessment indicators are used to evaluate the produced outcomes. An EEG dataset that Lomonosov Moscow State University released is used in experiments, and a 97.7% accuracy score is obtained. The obtained results are also compared with several recently published methods. The comparisons show that the proposed method outperforms the compared methods.

Bu çalışmada, elektrik güç sistemlerinde kısa devre arızaları, iletim hatları hesaplamaları, kesici açma gücü hesabı ve yük akış analizi için kullanılabilecek, Matlab/Guide tabanlı GSAP (Güç Sistemleri Analiz Programı) isimli bir program geliştirilmiştir. Geliştirilen bu paket program, Matlab programının sunduğu nümerik ve görsel imkânlardan faydalanmaktadır. Matlab yazılımı; kolay erişilebilmesi ve sunduğu paket programların zenginliğinden dolayı güç sistemlerinin dinamik analizlerinde en çok tercih edilen araçlardan biridir. GSAP’da geliştirilen kullanıcı ara yüzleri; kısa devre arızaları, iletim hatları hesaplamaları, kesici açma gücü hesabı ve yük akış analizlerinin kolayca yapılabilmesini sağlamaktadır. GSAP’ın etkinliği, doğruluğu ve başlıca özellikleri bazı örnek güç sistemleri uygulamaları ile test edilmiştir. Geliştirilen simülatör kullanıcı etkileşimli olup, lisans ve yüksek lisans seviyesindeki güç sistemleri analizi derslerini desteklemek için kullanılabilmektedir.