Purpose: Ischemic brain strokes have a high morbidity and death rate, thus it’s vital to obtain a quick diagnosis and imaging. Computer-aided diagnosis (CAD) has become popular in medical imaging and diagnostic radiology research. In the field of medical image analysis, deep learning (DL) approaches have recently shown greater performance over earlier, more advanced machine learning techniques. Acute Ischemic stroke (AIS) is one of the medical sectors where DL has conducted substantial research. The systematic review examines the performance of deep learning models across different imaging modalities, highlighting their strengths and limitations in identifying ischemic strokes. Key aspects such as sensitivity, specificity, and overall accuracy are assessed, providing insights into the comparative effectiveness of CT, MRI, and CTA in stroke detection. In contrast with other reviews in this domain, this paper offers a concise summary of the most notable DL methods applied in the classification, detection, and segmentation of acute ischemic brain stroke, focusing on popular imaging techniques like Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and CT Angiography (CTA). This survey also highlights datasets and data acquisition challenges and attempts to provide a comprehensive overview of data preprocessing, as well as insight into publicly available datasets.
Methods and Results: This study aims to give an idea of how training and testing datasets should be handled by evaluat- ing recent studies. This review discusses the challenges associated with each imaging modality, including image noise, artifacts, and variability in acquisition protocols. Strategies to mitigate these challenges through preprocessing techniques and model optimization are explored, aiming to improve the robustness and reliability of deep learning-based stroke de- tection systems. Moreover, this research contains a brief discussion of recent deep learning architectures and an analysis of performances.
Conclusions: Overall, this systematic review contributes to the understanding of current advancements in brain ischemic stroke detection through deep learning, offering valuable insights for researchers and clinicians seeking to leverage these technologies for improved patient outcomes. Future directions and potential research avenues are also discussed to guide further advancements in this critical area of medical imaging and diagnosis.

Error pattern recognition is a routine job in the military to provide corrective guidelines to the shooter. Errors can be recognized with a visual approach based on the spreading pattern of bullets on the target board, which are categorized into four categories: long horizontal error, long vertical error, bi-focal error, and scattered error. Currently, this process is performed manually and requires active human involvement. Similarly, an automated system to predict the future performance of a shooter is not available in the military domain. Moreover, the performance of a shooter depends on several factors, including age, weather, ammunition type, availability of light, previous scores, shooting range, classification of firing, and other factors. The military domain has not addressed the automatic prediction of such performance. While error correction and performance analysis have been extensively explored in the field of sports, their application within the military domain remains an untapped area of research and investigation. Numerous recent endeavors have suggested the utilization of deep learning to tackle this challenge. However, the absence of real-time data poses a significant obstacle, rendering these solutions seemingly impractical. In this paper, we have applied machine- learning approaches and adopted the best algorithm to automate the error pattern recognition system within a military domain. Our proposed methodology has two modules. The first module uses various algorithms and finds a random forest classifier that can do better to recognize the pattern of error and in the second phase, we used the AdaBoost classifier to predict the score and performance of a firer. Several experiments have been conducted, and the results show an average accuracy of 0.968 using Random Forest to recognize the pattern of error and an accuracy of 0.69 using AdaBoost to predict score performance. The data has been collected from the real-time environment of the military domain and experiments have been carried out using real-time scenarios with the military in mind.