Automatic detection of stroke lesion from diffusion-weighted imaging via the improved YOLOv5

Research on abnormal detection method of rail fastener based on YOLOV5

“…Figure 1 shows the overall block diagram of the YOLOv5 target detection algorithm [12]. For a target detection algorithm, we can usually divide it into four general modules: Input terminal, Benchmark network, Neck network, and Head Output terminal corresponding to the four red modules in the above figure.…”

Improved detection of aortic dissection in non-contrast-enhanced chest CT using an attention-based deep learning model

“…To improve diagnostic accuracy and efficiency, there is a need for deep learning techniques that can achieve accurate small-object detection. An attention mechanism can be added to a CNN architecture to enhance important features in an image while suppressing irrelevant features; this approach can result in enhanced accuracy in various computer vision tasks such as object detection, image categorisation, and semantic region identification in images [ [16] , [17] , [18] , [19] , [20] , [21] , [22] ].…”

Inception‐YOLO: Computational cost and accuracy improvement of the YOLOv5 model based on employing modified CSP, SPPF, and inception modules

“…Techniques for detecting objects have considerably advanced since convolutional neural networks (CNNs) were introduced and are being used in enormous applications and fields like pedestrian detection [17], healthcare [18], and virtual assistants [19]. The ability to recognize objects in pictures and videos is one of the core challenges in computer vision, and it is connected to a wide range of applications, such as face recognition [20], self-driving cars [21], traffic and vehicle detection [22], natural language processing [23], agriculture [24], medical image analysis [25,26] etc. But even with an extra localization assignment, it is challenging to precisely complete object identification because of the wide range of aspects, postures, and brightness conditions.…”