Results 1 to 10 of about 76,969 (165)

Accurate Detection of Alzheimer’s Disease Using Lightweight Deep Learning Model on MRI Data

open access: yesDiagnostics, 2023
Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by cognitive impairment and aberrant protein deposition in the brain. Therefore, the early detection of AD is crucial for the development of effective treatments and interventions, as
Ahmed A. Abd El-Latif   +3 more
doaj   +3 more sources

Force Map-Enhanced Segmentation of a Lightweight Model for the Early Detection of Cervical Cancer [PDF]

open access: yesDiagnostics
Background/Objectives: Accurate and efficient segmentation of cervical cells is crucial for the early detection of cervical cancer, enabling timely intervention and treatment.
Sabina Umirzakova   +3 more
doaj   +2 more sources

Pepper-YOLO: an lightweight model for green pepper detection and picking point localization in complex environments [PDF]

open access: yesFrontiers in Plant Science
In the cultivation of green chili peppers, the similarity between the fruit and background color, along with severe occlusion between fruits and leaves, significantly reduces the efficiency of harvesting robots.
Yikun Huang   +13 more
doaj   +2 more sources

DTONet a Lightweight Model for Melanoma Segmentation. [PDF]

open access: yesBioengineering (Basel)
With the further development of neural networks, automatic segmentation techniques for melanoma are becoming increasingly mature, especially under the conditions of abundant hardware resources. This allows for the accuracy of segmentation to be improved by increasing the complexity and computational capacity of the model.
Hao S   +6 more
europepmc   +4 more sources

3D model lightweight technology

open access: yesGong-kuang zidonghua, 2021
When the data volume of the 3D model increases, the model loading speed decreases and the browsing is not smooth. In order to solve the above problems, a 3D model lightweight technology based on the analysis of Cesium's model format 3DTiles is proposed ...
CHEN Long, GUO Jun, ZHANG Jianzhong
doaj   +1 more source

An Efficient Deep Learning Approach for Colon Cancer Detection

open access: yesApplied Sciences, 2022
Colon cancer is the second most common cause of cancer death in women and the third most common cause of cancer death in men. Therefore, early detection of this cancer can lead to lower infection and death rates.
Ahmed S. Sakr   +5 more
doaj   +1 more source

Enhancing Medical Image Denoising with Innovative Teacher–Student Model-Based Approaches for Precision Diagnostics

open access: yesSensors, 2023
The realm of medical imaging is a critical frontier in precision diagnostics, where the clarity of the image is paramount. Despite advancements in imaging technology, noise remains a pervasive challenge that can obscure crucial details and impede ...
Shakhnoza Muksimova   +3 more
doaj   +1 more source

Lightweight Intelligent Recognition of Saposhnikovia Divaricata (Turcz.) Schischk Originality Based on Improved ShuffleNet V2

open access: yes智慧农业, 2023
ObjectiveSaposhnikovia divaricata (Turcz.) Schischk is a kind of traditional Chinese medicine. Currently, the methods of identifying the origin and quality of Saposhnikovia divaricata (Turcz.) Schischk are mainly based on their physical or chemical ...
ZHAO Yu   +4 more
doaj   +1 more source

HRBUST-LLPED: A Benchmark Dataset for Wearable Low-Light Pedestrian Detection

open access: yesMicromachines, 2023
Detecting pedestrians in low-light conditions is challenging, especially in the context of wearable platforms. Infrared cameras have been employed to enhance detection capabilities, whereas low-light cameras capture the more intricate features of ...
Tianlin Li   +3 more
doaj   +1 more source

Eye Semantic Segmentation with A Lightweight Model [PDF]

open access: yes2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), 2019
In this paper, we present a multi-class eye segmentation method that can run the hardware limitations for real-time inference. Our approach includes three major stages: get a grayscale image from the input, segment three distinct eye region with a deep network, and remove incorrect areas with heuristic filters.
Van Thong Huynh   +3 more
openaire   +2 more sources

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