Artificial Intelligence
Amir Mahdi Sedghi; Shahla Nemati
Abstract
Background and Objectives: Brain tumor detection in MRI images is critical for early diagnosis and effective treatment, yet manual interpretation is time-consuming and prone to variability. Deep learning models such as YOLO have advanced real-time object detection, but their speed–accuracy tradeoff ...
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Background and Objectives: Brain tumor detection in MRI images is critical for early diagnosis and effective treatment, yet manual interpretation is time-consuming and prone to variability. Deep learning models such as YOLO have advanced real-time object detection, but their speed–accuracy tradeoff remains a challenge for medical tasks involving small or low-contrast lesions. The potential of transformer-based detectors like RT-DETR to simultaneously improve accuracy and maintain real-time speed in clinical settings is not well established.Methods: This study performed a controlled head-to-head comparison between the proposed model (RT-DETR-L-based model) and the YOLOv8s models using a curated, single-class brain tumor MRI dataset of 300 images. Both models were trained and evaluated under identical conditions with comprehensive data augmentation strategies, and their performance was assessed using standard object detection metrics including precision, recall, specificity, and mean Average Precision (mAP) across multiple IoU thresholds.Results: The proposed model achieved higher localization fidelity and overall accuracy compared to YOLOv8s, with mAP@0.5:0.95 of 0.493 versus 0.421 and mAP@0.5 of 0.963 versus 0.941. Precision and specificity for the proposed model reached 1.000, eliminating false positives, while recall was slightly lower than YOLOv8s (0.925 vs. 0.932), indicating a marginal increase in missed detections. Qualitative analysis confirmed robust detection across various tumor sizes and intensities, though some small or low-contrast lesions were missed.Conclusion: Proposed model surpasses YOLOv8s in accuracy and specificity for real-time brain tumor detection in MRI images, offering a promising balance between speed and precision. However, its slightly lower recall underscores the need for further refinement to minimize false negatives. The findings suggest transformer-based detectors can narrow the speed–accuracy gap in medical imaging, but broader validation and optimization for resource-constrained environments are required for clinical deployment. Future work should focus on enhancing sensitivity and generalizability through advanced augmentation, larger datasets, and ensemble approaches.
Computer Vision
Vincent Andrew Akpan; David Ayo-oluwa Adegoke; Ebunoluwa Temiloluwa Adejayan; Kehinde Adesola Adepoju
Abstract
Background and Objectives: The manual method of writing down vehicle plate numbers (VPNs), vehicle types, date, and time-stamps at the point of entry into and/or exit from the premises of organizations as well as the exit and/or entry time are not only time-consuming and stressful but are also prone ...
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Background and Objectives: The manual method of writing down vehicle plate numbers (VPNs), vehicle types, date, and time-stamps at the point of entry into and/or exit from the premises of organizations as well as the exit and/or entry time are not only time-consuming and stressful but are also prone to errors, delays, inconsistency and possible loss of hand-written data due to possible environmental hazards which makes this archaic method unreliable especially for security reasons. This study presents an artificial intelligence-based YOLOv3 using DarkNet-53 deep convolutional neural network (CNN) model architecture for the development of an automatic vehicle inventory system (AVIS) with a PostgreSQL-based dynamic relational database system (RDBS) for captured data storage and retrieval in real-time using Google cloud storage/retrieval.Methods: The AVIS with dynamic RDBS employs power-over-Ethernet (PoE) switch, PoE IP-based camera, Airtel router/Wi-Fi module and YOLOv3 using DarkNet-53 algorithm to capture and process VPNs from streaming video of moving vehicles. The processed results are stored in a properly designed dynamic RDBS over Google cloud storage system. The dynamic RDBS automatically creates and inserts all relevant vehicle information for security surveillance and tracking purposes. Several standard quantitative and qualitative metrics have been used to evaluate the performances of the YOLOv3 using DarkNet-53 architecture against YOLOv8 using CSPDarkNet-53 and YOLOv3 using SqueezeNet model architectures for comparison purposes.Results: Quantitatively, the YOLOv3 using DarkNet-53 and YOLOv8 using CSPDarkNet-53 achieved virtually equal performance metrics except for the excessive long execution time of 4.5839 hours used by YOLOv8 with CSPDarkNet-53compared to the 2.9713 minutes used by the YOLOv3 with DarkNet-53. The YOLOv3 with SqueezeNet used only 1.9901 minutes with relatively lower performance metric values. Qualitatively, successful and accurate LPNs detection and recognition with dynamic RDBS update to the cloud within 3 seconds for 25 random vehicles entering and/or exiting the premises of a car dealer company for a period of three days between 10:00am and 2:00pm daily has been achieved with YOLOv3 using DarkNet-53 model architecture.Conclusion: The proposed low-cost AVIS based on YOLOv3 using DarkNet-53 model architecture with a PostgreSQL dynamic RDBS and Google cloud storage have been successfully designed, implemented and validated with successful results for LPN detection and recognition. The proposed techniques offers promising potentials for timely and accurate data collection to optimize vehicle inventory management, control and operations for security surveillance design.
