Case based image retrieval and clinical analysis of tumor and cyst

Ayyachamy, Swarnambiga; Krishnamurthi, Ganapathy

doi:10.1117/12.2515660

Cited by 10 publications

(13 citation statements)

References 16 publications

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“…Therefore, it is difficult for a single sensory field to capture multilevel features of the dense block layers [38]. Meanwhile, ResNet-50 may also be influential in medical image categorization using residual networks in the same-sized object on an image [39]. In object detection with plantar pressure, images have more unusual object sizes, and loss of the image features may challenge ResNet-50 networks.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning in Left and Right Footprint Image Detection Based on Plantar Pressure

et al. 2022

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People with cerebral palsy (CP) suffer primarily from lower-limb impairments. These impairments contribute to the abnormal performance of functional activities and ambulation. Footprints, such as plantar pressure images, are usually used to assess functional performance in people with spastic CP. Detecting left and right feet based on footprints in people with CP is a challenge due to abnormal foot progression angle and abnormal footprint patterns. Identifying left and right foot profiles in people with CP is essential to provide information on the foot orthosis, walking problems, index gait patterns, and determination of the dominant limb. Deep learning with object detection can localize and classify the object more precisely on the abnormal foot progression angle and complex footprints associated with spastic CP. This study proposes a new object detection model to auto-determine left and right footprints. The footprint images successfully represented the left and right feet with high accuracy in object detection. YOLOv4 more successfully detected the left and right feet using footprint images compared to other object detection models. YOLOv4 reached over 99.00% in various metric performances. Furthermore, detection of the right foot (majority of people’s dominant leg) was more accurate than that of the left foot (majority of people’s non-dominant leg) in different object detection models.

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Section: Discussionmentioning

confidence: 99%

Deep Learning in Left and Right Footprint Image Detection Based on Plantar Pressure

et al. 2022

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“…The detailed model structure and settings were designed according to previous studies. 33,34 Finally, under the same data arrangement for the training and testing sets, one model with a deeper structure (ie, ResNet-50) 35,36 and the other with a shallower structure (ie, ResNet-18) 37,38 were adopted. The t-distributed stochastic neighbor-embedding (t-SNE) approach was used to compare the performance of these 2 structures.…”

Section: Procedures For DL Algorithm Developmentmentioning

confidence: 99%

Prediction of Visual Impairment in Epiretinal Membrane and Feature Analysis: A Deep Learning Approach Using Optical Coherence Tomography

Hsia

Lin

Wang

et al. 2023

Asia-Pacific Journal of Ophthalmology

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Purpose: The aim was to develop a deep learning model for predicting the extent of visual impairment in epiretinal membrane (ERM) using optical coherence tomography (OCT) images, and to analyze the associated features. Methods: Six hundred macular OCT images from eyes with ERM and no visually significant media opacity or other retinal diseases were obtained. Those with best-corrected visual acuity ≤ 20/50 were classified as "profound visual impairment," while those with bestcorrected visual acuity > 20/50 were classified as "less visual impairment." Ninety percent of images were used as the training data set and 10% were used for testing. Two convolutional neural network models (ResNet-50 and ResNet-18) were adopted for training. The t-distributed stochastic neighbor-embedding approach was used to compare their performances. The Grad-CAM technique was used in the heat map generative phase for feature analysis.Results: During the model development, the training accuracy was 100% in both convolutional neural network models, while the testing accuracy was 70% and 80% for ResNet-18 and ResNet-50, respectively. The t-distributed stochastic neighbor-embedding approach found that the deeper structure (ResNet-50) had better discrimination on OCT characteristics for visual impairment than the shallower structure (ResNet-18). The heat maps indicated that the key features for visual impairment were located mostly in the inner retinal layers of the fovea and parafoveal regions.Conclusions: Deep learning algorithms could assess the extent of visual impairment from OCT images in patients with ERM. Changes in inner retinal layers were found to have a greater impact on visual acuity than the outer retinal changes.

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“…The feature extraction network of CenterNet network usually uses Hourglass-104 [12] , DLA-34 [13] , ResNet-18 [14] and ResNet-101 networks, and the structure of DLA-34 combined with FPN is used as the backbone network in this paper.…”

Section: Figure 1 Principle Of Centernet Target Detectionmentioning

confidence: 99%

3D target detection based on dynamic occlusion processing

Peng¹,

Ma²,

Li³

2023

Third International Conference on Image Processing and Intelligent Control (IPIC 2023)

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In order to solve the multi-vehicle mutual occlusion problem encountered in 3D target detection by self-driving vehicles, this paper proposes a monocular 3D detection method that includes dynamic occlusion determination. The method adds a dynamic occlusion processing module to the CenterNet3D network framework to improve the accuracy of 3D target detection of occluded vehicles in the road. Specifically, the occlusion determination module of the method uses the 2D detection results extracted from target detection as the occlusion relationship determination condition, wherein the method of changing the occlusion determination threshold with the depth value is introduced. Then the occlusion compensation module is used to compensate and adjust the 3D detection results of the occurring occluded vehicles, and finally the 3D target detection results are output. The experimental results show that the method improves the accuracy of both vehicle center point detection and 3D dimensional detection results in the case of long-distance continuous vehicle occlusion. And compared with other existing methods, the accuracy of 3D detection results and bird's-eye view detection results are improved by 1%-2.64% in the case of intersection over union of 0.5. The method can compensate for the occluded vehicles in 3D target detection and improve the accuracy

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Case based image retrieval and clinical analysis of tumor and cyst

Cited by 10 publications

References 16 publications

Deep Learning in Left and Right Footprint Image Detection Based on Plantar Pressure

Deep Learning in Left and Right Footprint Image Detection Based on Plantar Pressure

Prediction of Visual Impairment in Epiretinal Membrane and Feature Analysis: A Deep Learning Approach Using Optical Coherence Tomography

3D target detection based on dynamic occlusion processing

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