Occlusion Detection Research Articles

Deep-Learning-Based Automated Building Construction Progress Monitoring for Prefabricated Prefinished Volumetric Construction.

Prefabricated prefinished volumetric construction (PPVC) is a relatively new technique that has recently gained popularity for its ability to improve flexibility in scheduling and resource management. Given the modular nature of PPVC assembly and the large amounts of visual data amassed throughout a construction project today, PPVC building construction progress monitoring can be conducted by quantifying assembled PPVC modules within images or videos. As manually processing high volumes of visual data can be extremely time consuming and tedious, building construction progress monitoring can be automated to be more efficient and reliable. However, the complex nature of construction sites and the presence of nearby infrastructure could occlude or distort visual data. Furthermore, imaging constraints can also result in incomplete visual data. Therefore, it is hard to apply existing purely data-driven object detectors to automate building progress monitoring at construction sites. In this paper, we propose a novel 2D window-based automated visual building construction progress monitoring (WAVBCPM) system to overcome these issues by mimicking human decision making during manual progress monitoring with a primary focus on PPVC building construction. WAVBCPM is segregated into three modules. A detection module first conducts detection of windows on the target building. This is achieved by detecting windows within the input image at two scales by using YOLOv5 as a backbone network for object detection before using a window detection filtering process to omit irrelevant detections from the surrounding areas. Next, a rectification module is developed to account for missing windows in the mid-section and near-ground regions of the constructed building that may be caused by occlusion and poor detection. Lastly, a progress estimation module checks the processed detections for missing or excess information before performing building construction progress estimation. The proposed method is tested on images from actual construction sites, and the experimental results demonstrate that WAVBCPM effectively addresses real-world challenges. By mimicking human inference, it overcomes imperfections in visual data, achieving higher accuracy in progress monitoring compared to purely data-driven object detectors.

A Flexible Self-Powered Occlusal Force Sensor Array for Assisting Oral Occlusion Reconstruction

Occlusal force is increasingly being recognized as a key parameter for evaluating masticatory motor dysfunction and the effectiveness of dental management in patients. However, the evaluation of occlusal force lacks objective quantitative assessment and reliable monitoring techniques. Here, a flexible self-powered sensor array is proposed for real-time monitoring occlusal force to further assist in oral occlusion reconstruction. Employing the triboelectric nanogenerator technology, the battery-free sensor succeeds in effectively capturing static and dynamic occlusal forces from teeth and converting them into accurate and quantitative electrical signals. With an average sensitivity of 0.72 volts per Newton and a response time of 15 milliseconds, the sensor features excellent synchronicity in electro-mechanical conversion and durability. Furthermore, the sensor array, based on complementary semicircular electrodes (15 × 15 pixels), enables the construction of visualized electrical signals mapping, which can reflect the intensity and location of occlusal force. The patterned electrical signal distribution can then be analyzed by researchers, providing a quantitative reference for clinical crown restoration. We expect this self-powered sensor array to enrich the approaches of occlusal force detection and provide valuable assistance for the reconstruction of oral occlusion.

Angiographic reconstructions of CT perfusion for occlusion detection in ischemic stroke

BackgroundBoth CT angiography and CT perfusion involve the administration and tracking of a contrast medium bolus for different purposes. In this study, we aim to compare the diagnostic accuracy and subjective image quality of CTP-angiographic reconstructions with conventional CTA for occlusion detection in ischemic stroke patients. MethodsIn this retrospective study, patients with a final diagnosis of ischemic stroke and who underwent both CTA and CTP from September 2020 up to and including September 2021 were included. CTP-AR was reconstructed from the 1 mm CTP series at the time of maximum arterial inflow. Three readers with different levels of experience assessed both CTA and CTP for occlusion detection, expressed certainty, and rated subjective image quality. The reference standard was set at the consensus meeting. Pooled sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. Differences in subjective and objective image quality and certainty were assessed using Kendall’s tau correlation and paired samples t-tests. ResultsIn total, 107/210 included patients had an occlusion based on our reference standard. Pooled sensitivity and specificity for occlusion detection were 90 % (95 %CI 85–93 %) and 94 % (95 %CI 90–97 %) for CTA, and 89 % (95 %CI 84–93 %) and 93 % (95 %CI 89–96) for CTP-AR, respectively. The pooled certainty did not significantly differ between CTA and CTP-AR (P=.43). The pooled subjective image quality scores significantly differed between CTA and CTP-AR (τ = 0.3, P < 0.001), where one reader rated subjective image quality higher in CTA and two readers in CTP-AR. ConclusionCTP-AR has comparable diagnostic accuracy to conventional CTA imaging for occlusion detection in ischemic stroke patients. Thus, using CTP-AR instead of CTA plus CTP may conserve contrast medium.

Introduction and accuracy assessment of Nicolab’s StrokeViewer in a developing stroke thrombectomy UK service. A Service Development / Improvement Project.

AimTo evaluate the implementation of an AI software in a quaternary stroke center as well as assess the accuracy and efficacy of StrokeViewer software in large vessel occlusion detection and potential impact on radiological workflow. Materials and methodsData were collected during two separate three months’ periods comparing the accuracy rate of StrokeViewer in large vessel occlusion to a junior registrar. During the first three months, 37 cases were identified and during the second leg 47. The second leg of the study was performed due to a high number of technical failures during the first one and an attempt to improve those via communication with the manufacturer and co-operation between allied healthcare professionals. Statistical analysis was performed using SPSS software. ResultsTechnical failure rate was 25% in the first leg and reduced to 17% in the second leg, showing a trend to statistical significance. Specificity and sensitivity of StrokeViewer were similar in the two legs of the study, measuring 91% and 93% initially and 94% and 93% finally, respectively. Efficacy was comparable to that of the junior registrar with StrokeViewer demonstrating 92% accuracy during the first leg vs 95% by the junior registrar and 93% in the second leg vs 98% by the junior registrar. These did not show statistical significance. ConclusionThis is a real-life analysis of StrokeViewer efficacy and its potential failures, showing a reduction in failure rate, accuracy rate of a junior registrar and sensitivity and specificity values close to the advertised ones.

Automated Detection of Central Retinal Artery Occlusion Using OCT Imaging via Explainable Deep Learning

ObjectiveTo demonstrate the capability of a deep learning model to detect central retinal artery occlusion (CRAO), a retinal pathology with significant clinical urgency, using optical coherence tomography (OCT) data. DesignRetrospective, external validation study analyzing OCT and clinical baseline data of two institutions via deep learning classification analysis. SubjectsPatients presenting to the University Medical Center Tübingen and the University Medical Center Hamburg-Eppendorf in Germany. MethodsOCT-data of patients suffering from CRAO, differential diagnosis with (sub-)acute visual loss (central retinal vein occlusion, diabetic macular edema, non-arteritic ischemic optic neuropathy) and from controls were expertly graded and distinguished into the three groups.Our methodological approach involved a nested multiclass 5-fold cross-validation classification scheme. Main Outcome MeasuresArea under the curve (AUC) ResultsThe optimal performance of our algorithm was observed using 30 epochs, complemented by an early stopping mechanism to prevent overfitting. Our model followed a multiclass approach, distinguishing among the three different classes: Control CRAO and differential diagnoses. The evaluation was conducted by the “one vs. all” area under the receiver operating characteristics (ROC) curve (AUC) method. The results demonstrated AUC of 0.96 (95% confidence interval (CI): ± 0.01), 0.99 (95% CI: ± 0.00), 0.90 (95% CI: ± 0.03) for each class, respectively. ConclusionsOur machine learning algorithm (MLA) exhibited a high AUC, as well as sensitivity and specificity in detecting CRAO and the differential classes, respectively. These findings underscore the potential for deploying MLAs in the identification of less common etiologies within an acute emergency clinical setting.

Performance of An Automated Algorithm in Large and Medium Vessel Occlusion Detection: A Real-World Experience.

Fast, accurate detection of large (LVO) and medium vessel occlusion (MeVO) is critical for triage and management of acute ischemic stroke. Multiple AIbased software products are commercially available. However, their strengths and limitations for detection of vessel occlusion in the context of expanding indications for mechanical thrombectomy are not entirely understood. We aimed to investigate the performance of a fully automated commercial detection algorithm to identify large and medium vessel occlusions in Code Stroke patients. We utilized a single-center, institutional, retrospective registry of all consecutive code stroke patients with CTA and automated processing using Viz.ai presenting at a comprehensive stroke center between March 2020 and February 2023. LVO was categorized as anterior LVO (aLVO), defined as occlusion of the intracranial internal cerebral artery or M1-middle cerebral artery (MCA), and posterior LVO (pLVO), defined as occlusion of the basilar artery or V4-vertebral artery. MeVO was defined as occlusion of the M2-MCA, A1/A2-anterior cerebral artery, or P1/P2-posterior cerebral artery. Reports from 12 board-certified radiologists were considered the gold standard. We analyzed the performance of the automated algorithm using STARD guidelines. Our primary outcome was accuracy of anterior LVO (aLVO) by the software. Secondary outcomes were accuracy of the software to detect three additional categories: all LVO (aLVO and pLVO), aLVO with M2-MCA, and aLVO with MeVO. Of 3,590 code stroke patients, 3,576 were technically sufficient for analysis by the automated software (median age 67 years; 51% female; 68% White), of which 616 (17.2%) had vessel occlusions. The respective sensitivity and specificity for all four pre-specified categories were: aLVO: 91% (87-94%), 93% (92-94%); all LVO: 73% (68-77%), 92% (91-93%); aLVO with M2-MCA:74% (70-78%), 93% (92-94%); aLVO with all MeVO: 65% (61-69%), and 93% (92-94%). The automated algorithm demonstrated high accuracy in identifying anterior LVO with lower performance for pLVO and MeVO. It is crucial for acute stroke teams to be aware of the discordance between automated algorithm results and true rates of LVO and MeVO for timely diagnosis and triage. LVO = large vessel occlusion; aLVO = anterior large vessel occlusion; pLVO = posterior large vessel occlusion; MeVO = medium vessel occlusion; EVT= endovascular thrombectomy; AI = artificial intelligence; ACA = anterior cerebral artery; PCA = posterior cerebral artery; BA = basilar artery; VA = vertebral artery.

On the detection of acute coronary occlusion with the miniECG

On the detection of acute coronary occlusion with the miniECG

Automated detection of large vessel occlusion using deep learning: a pivotal multicenter study and reader performance study

BackgroundTo evaluate the stand-alone efficacy and improvements in diagnostic accuracy of early-career physicians of the artificial intelligence (AI) software to detect large vessel occlusion (LVO) in CT angiography (CTA).MethodsThis multicenter...

Detection of acute coronary occlusion with a novel mobile electrocardiogram device: a pilot study: reply

Detection of acute coronary occlusion with a novel mobile electrocardiogram device: a pilot study: reply

YOLOv8-E: An Improved YOLOv8 Algorithm for Eggplant Disease Detection

During the developmental stages, eggplants are susceptible to diseases, which can impact crop yields and farmers’ economic returns. Therefore, timely and effective detection of eggplant diseases is crucial. Deep learning-based object detection algorithms can automatically extract features from images of eggplants affected by diseases. However, eggplant disease images captured in complex farmland environments present challenges such as varying disease sizes, occlusion, overlap, and small target detection, making it difficult for existing deep-learning models to achieve satisfactory detection performance. To address this challenge, this study proposed an optimized eggplant disease detection algorithm, YOLOv8-E, based on You Only Look Once version 8 nano (YOLOv8n). Firstly, we integrate switchable atrous convolution (SAConv) into the C2f module to design the C2f_SAConv module, replacing some of the C2f modules in the backbone network of YOLOv8n, enabling our proposed algorithm to better extract eggplant disease features. Secondly, to facilitate the deployment of the detection model on mobile devices, we reconstruct the Neck network of YOLOv8n using the SlimNeck module, making the model lighter. Additionally, to tackle the issue of missing small targets, we embed the large separable kernel attention (LSKA) module within SlimNeck, enhancing the model’s attention to fine-grained information. Lastly, we combined intersection over union with auxiliary bounding box (Inner-IoU) and minimum point distance intersection over union (MPDIoU), introducing the Inner-MPDIoU loss to speed up convergence of the model and raise detection precision of overlapped and occluded targets. Ablation studies demonstrated that, compared to YOLOv8n, the mean average precision (mAP) and F1 score of YOLOv8-E reached 79.4% and 75.7%, respectively, which obtained a 5.5% increment and a 4.5% increase, while also reducing the model size and computational complexity. Furthermore, YOLOv8-E achieved higher detection performance than other mainstream algorithms. YOLOv8-E exhibits significant potential for practical application in eggplant disease detection.

Artificial Intelligence Efficacy as a Function of Trainee Interpreter Proficiency: Lessons from a Randomized Controlled Trial.

Recently, artificial intelligence tools have been deployed with increasing speed in educational and clinical settings. However, the use of artificial intelligence by trainees across different levels of experience has not been well-studied. This study investigates the impact of artificial intelligence assistance on the diagnostic accuracy for intracranial hemorrhage and large-vessel occlusion by medical students and resident trainees. This prospective study was conducted between March 2023 and October 2023. Medical students and resident trainees were asked to identify intracranial hemorrhage and large-vessel occlusion in 100 noncontrast head CTs and 100 head CTAs, respectively. One group received diagnostic aid simulating artificial intelligence for intracranial hemorrhage only (n = 26); the other, for large-vessel occlusion only (n = 28). Primary outcomes included accuracy, sensitivity, and specificity for intracranial hemorrhage/large-vessel occlusion detection without and with aid. Study interpretation time was a secondary outcome. Individual responses were pooled and analyzed with the t test; differences in continuous variables were assessed with ANOVA. Forty-eight participants completed the study, generating 10,779 intracranial hemorrhage or large-vessel occlusion interpretations. With diagnostic aid, medical student accuracy improved 11.0 points (P < .001) and resident trainee accuracy showed no significant change. Intracranial hemorrhage interpretation time increased with diagnostic aid for both groups (P < .001), while large-vessel occlusion interpretation time decreased for medical students (P < .001). Despite worse performance in the detection of the smallest-versus-largest hemorrhages at baseline, medical students were not more likely to accept a true-positive artificial intelligence result for these more difficult tasks. Both groups were considerably less accurate when disagreeing with the artificial intelligence or when supplied with an incorrect artificial intelligence result. This study demonstrated greater improvement in diagnostic accuracy with artificial intelligence for medical students compared with resident trainees. However, medical students were less likely than resident trainees to overrule incorrect artificial intelligence interpretations and were less accurate, even with diagnostic aid, than the artificial intelligence was by itself.

Evaluation of the Accuracy of Different Occlusal Caries Detection Methods: A Comparative Study

Evaluation of the Accuracy of Different Occlusal Caries Detection Methods: A Comparative Study

Reliability of impedance spectroscopy versus digital radiograph and ICDAS-II in occlusal caries detection: a prospective clinical trial

The traditional methods in early caries detection had many limitations. So, this study aimed to evaluate the clinical performance of alternating current impedance spectroscopy ACIST in comparison with digital radiograph and ICDAS-II in detection of occlusal carious lesions. Occlusal surfaces of molar and premolar teeth from 40 adult participants were examined by two observers using three diagnostic methods: (1) international caries detection and assessment system (ICDAS-II) (2) digital radiograph (DR) and (3) Cariescan Pro device (ACIST). Agreement analysis and the difference in sensitivities and specificities were evaluated. The results showed an excellent agreement in the different groups. The difference from the visual tactile against ACIST scoring for enamel caries detection, was statistically significant (p = 0.012) and the agreement was moderate (k = 0.509). For dentinal caries the difference was not statistically significant (p > 0.05) and the agreement was similarly moderate (k < 0.6). The difference from the digital radiograph against ACIST scoring, for enamel caries, digital radiography had significantly higher sensitivity and specificity than ACIST (p < 0.001) while for dentinal caries detection and overall, ACIST had higher sensitivity and digital radiography had higher specificity and the difference was statistically significant (p < 0.001). Visual-tactile examination is a considered as feasible and valid technique for occlusal caries detection, digital radiography is superior to ACIST in diagnosing enamel caries, but it could underestimate the caries depth, ACIST is a reliable tool for detecting occlusal caries in dentin.

Artificial intelligence in ischemic stroke images: current applications and future directions.

This paper reviews the current research progress in the application of Artificial Intelligence (AI) based on ischemic stroke imaging, analyzes the main challenges, and explores future research directions. This study emphasizes the application of AI in areas such as automatic segmentation of infarct areas, detection of large vessel occlusion, prediction of stroke outcomes, assessment of hemorrhagic transformation risk, forecasting of recurrent ischemic stroke risk, and automatic grading of collateral circulation. The research indicates that Machine Learning (ML) and Deep Learning (DL) technologies have tremendous potential for improving diagnostic accuracy, accelerating disease identification, and predicting disease progression and treatment responses. However, the clinical application of these technologies still faces challenges such as limitations in data volume, model interpretability, and the need for real-time monitoring and updating. Additionally, this paper discusses the prospects of applying large language models, such as the transformer architecture, in ischemic stroke imaging analysis, emphasizing the importance of establishing large public databases and the need for future research to focus on the interpretability of algorithms and the comprehensiveness of clinical decision support. Overall, AI has significant application value in the management of ischemic stroke; however, existing technological and practical challenges must be overcome to achieve its widespread application in clinical practice.

Systematic Review and Meta-Analysis of Prehospital Machine Learning Scores as Screening Tools for Early Detection of Large Vessel Occlusion in Patients With Suspected Stroke.

Enhanced detection of large vessel occlusion (LVO) through machine learning (ML) for acute ischemic stroke appears promising. This systematic review explored the capabilities of ML models compared with prehospital stroke scales for LVO prediction. Six bibliographic databases were searched from inception until October 10, 2023. Meta-analyses pooled the model performance using area under the curve (AUC), sensitivity, specificity, and summary receiver operating characteristic curve. Of 1544 studies screened, 8 retrospective studies were eligible, including 32 prehospital stroke scales and 21 ML models. Of the 9 prehospital scales meta-analyzed, the Rapid Arterial Occlusion Evaluation had the highest pooled AUC (0.82 [95% CI, 0.79-0.84]). Support Vector Machine achieved the highest AUC of 9 ML models included (pooled AUC, 0.89 [95% CI, 0.88-0.89]). Six prehospital stroke scales and 10 ML models were eligible for summary receiver operating characteristic analysis. Pooled sensitivity and specificity for any prehospital stroke scale were 0.72 (95% CI, 0.68-0.75) and 0.77 (95% CI, 0.72-0.81), respectively; summary receiver operating characteristic curve AUC was 0.80 (95% CI, 0.76-0.83). Pooled sensitivity for any ML model for LVO was 0.73 (95% CI, 0.64-0.79), specificity was 0.85 (95% CI, 0.80-0.89), and summary receiver operating characteristic curve AUC was 0.87 (95% CI, 0.83-0.89). Both prehospital stroke scales and ML models demonstrated varying accuracies in predicting LVO. Despite ML potential for improved LVO detection in the prehospital setting, application remains limited by the absence of prospective external validation, limited sample sizes, and lack of real-world performance data in a prehospital setting.

Identifying the Sylvian Triangle on CT angiography: A technique for detecting distal middle cerebral artery occlusions.

Medium vessel occlusions (MeVOs), defined as occlusion of the M2/M3 and A2/A3 segments of the middle cerebral artery (MCA) and anterior cerebral artery, can be challenging to visualize on CT angiography (CTA) and MR angiography (MRA), given the anatomic complexity of the mid- and distal intracranial vasculature and smaller vessel caliber (Leary MC, Kidwell CS, Villablanca JP, et al. Validation of computed tomographic MCA "dot" sign: an angiographic correlation study. Stroke 2003; 34: 2636-2640; Luijten SPR, Wolff L, Duvekot MHC, et al. Diagnostic performance of an algorithm for automated large vessel occlusion (LVO) detection on CTA. J Neurointerv Surg 2022; 14: 794-798). In turn, the appearance of a sudden vessel cutoff in these vascular distributions on CTA or MRA is not always straightforward and may represent true occlusion, variant anatomy, and/or artifact (Leary MC, Kidwell CS, Villablanca JP, et al. Validation of computed tomographic MCA "dot" sign: an angiographic correlation study. Stroke 2003; 34: 2636-2640; Luijten SPR, Wolff L, Duvekot MHC, et al. Diagnostic performance of an algorithm for automated LVO detection on CTA. J Neurointerv Surg 2022; 14: 794-798). Given the importance of rapidly establishing an accurate diagnosis in the setting of stroke, combined with recent clinical trials and movements promoting the efficacy of endovascular therapeutic approaches to treat MeVOs, it remains imperative to detect such occlusions accurately and quickly on imaging. In turn, we present five imaging patterns of the Sylvian Triangle on sagittal reformatted images from CTA Head examinations, which our practice has utilized to assess patency of the M2 and M3 divisions. This approach is rapidly deployable and can be utilized by radiology and non-radiology healthcare providers alike, thus facilitating rapid and accurate diagnosis of MeVO, timely evaluation of candidacy for endovascular therapy, and ultimately supporting favorable door-to-intervention time and successful patient outcomes.

LSTM-QDCNN: long short-term memory and quantum dilated convolutional neural network enabled occlusion percentage prediction

ABSTRACT Occlusion detection is vital in longer video sequences of real-time scenarios, as deformable objects generally occlude themselves or be occluded by exterior objects eventually. Occlusion can affect accuracy of the entire model significantly, because optical flow may reveal random attributes in occluded as well as neighbourhood areas. Occlusion is a challenging issue in real-world visual object tracking. Several techniques learn imprecise appearance of target while it becomes occluded by another object in the scene. Occlusion is a complicated issue that causes target loss in tracking procedure. Here, long short-term memory fused quantum dilated convolutional neural network (LSTM-QDCNN) is presented for occlusion percentage prediction. Initially, video frames are extracted from considered input video. The extracted frame is pre-processed by adaptive fuzzy filtering, and object localisation is accomplished by employing sparse fuzzy c-means with local optimal oriented pattern feature. Thereafter, occlusion category detection is conducted by deep Q-network. Lastly, occlusion percentage prediction is done by utilising LSTM-QDCNN. Additionally, LSTM-QDCNN is designed by merging deep LSTM (DLSTM) with quantum dilated convolutional neural network (QDCNN). Furthermore LSTM-QDCNN achieved maximum multiple-object tracking precision of 0.892 and minimum tracking distance of 3.714. In addition, LSTM-QDCNN achieved tracking number about 18.

Viz LVO versus Rapid LVO in detection of large vessel occlusion on CT angiography for acute stroke

BackgroundEndovascular thrombectomy improves outcomes and reduces mortality for large vessel occlusion (LVO) and is time-sensitive. Computer automation may aid in the early detection of LVOs, but false values may lead...

Implementation of hand‐object pose estimation using SSD and YOLOV5 model for object grasping by SCARA robot

AbstractEnforcement of advanced deep learning methods in hand‐object pose estimation is an imperative method for grasping the objects safely during the human–robot collaborative tasks. The position and orientation of a hand‐object from a two‐dimensional image is still a crucial problem under various circumstances like occlusion, critical lighting, and salient region detection and blur images. In this paper, the proposed method uses an enhanced MobileNetV3 with single shot detection (SSD) and YOLOv5 to ensure the improvement in accuracy and without compromising the latency in the detection of hand‐object pose and its orientation. To overcome the limitations of higher computation cost, latency and accuracy, the Network Architecture Search and NetAdapt Algorithm is used in MobileNetV3 that perform the network search for parameter tuning and adaptive learning for multiscale feature extraction and anchor box offset adjustment due to auto‐variance of weight in the level of each layers. The squeeze‐and‐excitation block reduces the computation and latency of the model. Hard‐swish activation function and feature pyramid networks are used to prevent over fitting the data and stabilizing the training. Based on the comparative analysis of MobileNetV3 with its predecessor and YOLOV5 are carried out, the obtained results are 92.8% and 89.7% of precision value, recall value of 93.1% and 90.2%, mAP value of 93.3% and 89.2%, respectively. The proposed methods ensure better grasping for robots by providing the pose estimation and orientation of hand‐objects with tolerance of −1.9 to 2.15 mm along x, −1.55 to 2.21 mm along y, −0.833 to 1.51 mm along z axis and −0.233° to 0.273° along z‐axis.

Large vessel occlusion detection by non-contrast CT using artificial ıntelligence

IntroductionComputer vision models have been used to diagnose some disorders using computer tomography (CT) and magnetic resonance (MR) images. In this work, our objective is to detect large and small brain vessel occlusion using a deep feature engineering model in acute of ischemic stroke.MethodsWe use our dataset. which contains 324 patient’s CT images with two classes; these classes are large and small brain vessel occlusion. We divided the collected image into horizontal and vertical patches. Then, pretrained AlexNet was utilized to extract deep features. Here, fc6 and fc7 (sixth and seventh fully connected layers) layers have been used to extract deep features from the created patches. The generated features from patches have been concatenated/merged to generate the final feature vector. In order to select the best combination from the generated final feature vector, an iterative selector (iterative neighborhood component analysis—INCA) has been used, and this selector has chosen 43 features. These 43 features have been used for classification. In the last phase, we used a kNN classifier with tenfold cross-validation.ResultsBy using 43 features and a kNN classifier, our AlexNet-based deep feature engineering model surprisingly attained 100% classification accuracy.ConclusionThe obtained perfect classification performance clearly demonstrated that our proposal could separate large and small brain vessel occlusion detection in non-contrast CT images. In this aspect, this model can assist neurology experts with the early recanalization chance.