Deep Learning-Based Clinical Wound Image Analysis Using a Mask R-CNN Architecture

Huang, Shong; Chu, Yu-Chang; Liu, Liong-Rung; Yao, Wen‐Teng; Chen, Yu-Fan; Yu, Chieh‐Ming; Yu, Chia‐Meng; Tung, Kwang‐Yi; Chiu, Hsin–Hui; Tsai, Ming-Feng

doi:10.1007/s40846-023-00802-2

Cited by 4 publications

(2 citation statements)

References 29 publications

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“…The findings indicate that the method proposed by the researchers demonstrates effective utility as a decision support system for wound image classification and other clinically relevant applications. Huang et al ( 32 ) proposed an innovative approach to automatically segment and detect wounds by leveraging the Mask R-CNN framework. Their study employed a dataset comprising 3,329 clinical wound images, encompassing wounds observed in patients diagnosed with peripheral artery disease, as well as those resulting from general trauma.…”

Section: Related Workmentioning

confidence: 99%

Automated wound care by employing a reliable U-Net architecture combined with ResNet feature encoders for monitoring chronic wounds

Alabdulhafith,

Ba Mahel,

Samee

et al. 2024

Front. Med.

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Quality of life is greatly affected by chronic wounds. It requires more intensive care than acute wounds. Schedule follow-up appointments with their doctor to track healing. Good wound treatment promotes healing and fewer problems. Wound care requires precise and reliable wound measurement to optimize patient treatment and outcomes according to evidence-based best practices. Images are used to objectively assess wound state by quantifying key healing parameters. Nevertheless, the robust segmentation of wound images is complex because of the high diversity of wound types and imaging conditions. This study proposes and evaluates a novel hybrid model developed for wound segmentation in medical images. The model combines advanced deep learning techniques with traditional image processing methods to improve the accuracy and reliability of wound segmentation. The main objective is to overcome the limitations of existing segmentation methods (UNet) by leveraging the combined advantages of both paradigms. In our investigation, we introduced a hybrid model architecture, wherein a ResNet34 is utilized as the encoder, and a UNet is employed as the decoder. The combination of ResNet34’s deep representation learning and UNet’s efficient feature extraction yields notable benefits. The architectural design successfully integrated high-level and low-level features, enabling the generation of segmentation maps with high precision and accuracy. Following the implementation of our model to the actual data, we were able to determine the following values for the Intersection over Union (IOU), Dice score, and accuracy: 0.973, 0.986, and 0.9736, respectively. According to the achieved results, the proposed method is more precise and accurate than the current state-of-the-art.

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Section: Related Workmentioning

confidence: 99%

Automated wound care by employing a reliable U-Net architecture combined with ResNet feature encoders for monitoring chronic wounds

Alabdulhafith,

Ba Mahel,

Samee

et al. 2024

Front. Med.

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“…Convolutional neural networks (CNNs) utilize an operation method that eliminates the necessity for manual feature selection, rendering them highly effective in the domain of image processing [ 11 ]. Originally introduced in 1990 for the recognition of handwritten digits, researchers are currently incorporating artificial intelligence powered Computer-Aided Diagnosis (CAD) systems into medical imaging to facilitate early diagnosis and intervention by physicians [ 12 ]. CNNs have recently demonstrated their potential in accurately classifying various types of arrhythmias and ECG signals [ [13] , [14] , [15] , [16] ].…”

Section: Introductionmentioning

confidence: 99%

An Arrhythmia classification approach via deep learning using single-lead ECG without QRS wave detection

Liu,

Huang,

Huang

et al. 2024

Heliyon

Self Cite

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Design of Logistics Sorting Algorithm Based on Deep Learning and Sampling Evaluation

Feng

2024

Int J Comput Intell Syst

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The advancement of automation and Internet of Things technology has bolstered the automation process in the logistics sector. To address the challenge of localizing and generating grasping positions for intelligent robots in logistics sorting, this study developed an algorithm for item localization. The algorithm relies on enhanced YOLOv3 target detection and instance segmentation technologies to design a position generation algorithm for the robotic arm, which was further refined using sampling evaluation. The experimental results showed that the research-improved target detection model performed better on different datasets in terms of F1 value, accuracy and Area under the Curve (AUC) metrics, with the highest values of 95.77%, 94.05%, and 91.30%, respectively, which was effective in localizing document-like parcels. Meanwhile, the instance segmentation algorithm with fused features took significantly lower values than other target detection algorithms in terms of average absolute value error and root mean square error. The accuracy rate and all-class average precision value were higher than other target detection models, and the fluctuation of the value taken was smaller, which was suitable for logistics parcel localization. The position generation model, based on a sampling evaluation, yielded significantly different values compared to other algorithms. The relative position error and absolute trajectory error indexes were all below 0.4. The combined indexes of grasping accuracy and error indicate the superior performance of the research-designed algorithms. They can effectively enhance the sorting effects of real logistics scenarios. This research contributes to the improvement of the automated sorting system through the use of visual robotic arm technology. Additionally, it encourages the development of logistics automation and the establishment of intelligent logistics factories.

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Deep Learning-Based Clinical Wound Image Analysis Using a Mask R-CNN Architecture

Cited by 4 publications

References 29 publications

Automated wound care by employing a reliable U-Net architecture combined with ResNet feature encoders for monitoring chronic wounds

Automated wound care by employing a reliable U-Net architecture combined with ResNet feature encoders for monitoring chronic wounds

An Arrhythmia classification approach via deep learning using single-lead ECG without QRS wave detection

Design of Logistics Sorting Algorithm Based on Deep Learning and Sampling Evaluation

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