Diabetic retinopathy (DR) is a disease resulting from diabetes complications, causing non-reversible damage to retina blood vessels. DR is a leading cause of blindness if not detected early. The currently available DR treatments are limited to stopping or delaying the deterioration of sight, highlighting the importance of regular scanning using high-efficiency computer-based systems to diagnose cases early. The current work presented fully automatic diagnosis systems that exceed manual techniques to avoid misdiagnosis, reducing time, effort and cost. The proposed system classifies DR images into five stages—no-DR, mild, moderate, severe and proliferative DR—as well as localizing the affected lesions on retain surface. The system comprises two deep learning-based models. The first model (CNN512) used the whole image as an input to the CNN model to classify it into one of the five DR stages. It achieved an accuracy of 88.6% and 84.1% on the DDR and the APTOS Kaggle 2019 public datasets, respectively, compared to the state-of-the-art results. Simultaneously, the second model used an adopted YOLOv3 model to detect and localize the DR lesions, achieving a 0.216 mAP in lesion localization on the DDR dataset, which improves the current state-of-the-art results. Finally, both of the proposed structures, CNN512 and YOLOv3, were fused to classify DR images and localize DR lesions, obtaining an accuracy of 89% with 89% sensitivity, 97.3 specificity and that exceeds the current state-of-the-art results.

The current era is characterized by the rapidly increasing use of computer-aided diagnosis (CAD) systems in the medical field. These systems need a variety of datasets to help develop, evaluate, and compare their performances fairly. Physicians indicated that breast anatomy, especially dense ones, and the probability of breast cancer and tumor development, vary highly depending on race. Researchers reported that breast cancer risk factors are related to culture and society. Thus, there is a massive need for a local dataset representing breast cancer in our region to help develop and evaluate automatic breast cancer CAD systems. This paper presents a public mammogram dataset called King Abdulaziz University Breast Cancer Mammogram Dataset (KAU-BCMD) version 1. To our knowledge, KAU-BCMD is the first dataset in Saudi Arabia that deals with a large number of mammogram scans. The dataset was collected from the Sheikh Mohammed Hussein Al-Amoudi Center of Excellence in Breast Cancer at King Abdulaziz University. It contains 1416 cases. Each case has two views for both the right and left breasts, resulting in 5662 images based on the breast imaging reporting and data system. It also contains 205 ultrasound cases corresponding to a part of the mammogram cases, with 405 images as a total. The dataset was annotated and reviewed by three different radiologists. Our dataset is a promising dataset that contains different imaging modalities for breast cancer with different cancer grades for Saudi women.

Modern cities have imposed a fast-paced lifestyle where more drivers on the road suffer from fatigue and sleep deprivation. Consequently, road accidents have increased, becoming one of the leading causes of injuries and death among young adults and children. These accidents can be prevented if fatigue symptoms are diagnosed and detected sufficiently early. For this reason, we propose and compare two AlexNet CNN-based models to detect drivers’ fatigue behaviors, relying on head position and mouth movements as behavioral measures. We used two different approaches. The first approach is transfer learning, specifically, fine-tuning AlexNet, which allowed us to take advantage of what the model had already learned without developing it from scratch. The newly trained model was able to predict drivers’ drowsiness behaviors. The second approach is the use of AlexNet to extract features by training the top layers of the network. These features were reduced using non-negative matrix factorization (NMF) and classified with a support vector machine (SVM) classifier. The experiments showed that our proposed transfer learning model achieved an accuracy of 95.7%, while the feature extraction SVM-based model performed better, with an accuracy of 99.65%. Both models were trained on a simulated NTHU Driver Drowsiness Detection dataset.

Decreasing road accidents rate and increasing road safety have been the major concerns for a long time as traffic accidents expose the divers, passengers, properties to danger. Driver fatigue and drowsiness are one of the most critical factors affecting road safety, especially on highways. EEG signal is one of the reliable physiological signals used to perceive driver fatigue state but wearing a multi-channel headset to acquire the EEG signal limits the EEG based systems among drivers. The current work suggested using a driver fatigue detection system using transfer learning, depending only on one EEG channel to increase system usability. The system firstly acquires the signal and passing it through preprocessing filtering then, converts it to a 2D spectrogram. Finally, the 2D spectrogram is classified with AlexNet using transfer learning to classify it either normal or fatigue state. The current study compares the accuracy of seven EEG channel to select one of them as the most accurate channel to depend on it for classification. The results show that the channels FP1 and T3 are the most effective channels to indicate the drive fatigue state. They achieved an accuracy of 90% and 91% respectively. Therefore, using only one of these channels with the modified AlexNet CNN model can result in an efficient driver fatigue detection system.

This paper aims to evaluate EEG as an application predictor of Holland test with career interest. Consequently, it is essential to develop and analyze the brain wave of clinical changes of the EEG signals during Holland answering questions. The experimental test begins by answering Holland test with 34 participants of staff members at KAU (College of Computing and Information Technology and College of Engineering). Accordingly, the test is applied twice, one without using EEG and the second with EEG recording. The proposed solution uses 34 answering of participants of Holland career test dataset after getting the content data from the answering of Holland using the EEG and initial career assessment. Consequently, the Holland answering test (without EEG) is analyzed along with EEG analysis of the Holland answering in different frequency bands such as delta, theta, alpha and beta. Therefore, we aim to provide researchers, a methodology, if we could identify career interest using EEG signals or to determine the level of contribution of EEG signals would support identifying career types.