Automated Detection of Gastric Lesions in Endoscopic Images by Leveraging Attention-Based YOLOv7

Ahmad, Sarmad Zaheer; Kim, Jae-Seoung; Park, Dong Kyun; Whangbo, Taeg Keun

doi:10.1109/access.2023.3296710

Cited by 7 publications

(2 citation statements)

References 37 publications

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“…While YOLO is traditionally an object detection framework, YOLOv7 has been modified for instance segmentation use cases [17]. This architecture has been used to develop segmentation models for self-driving cars [18] as well as some medical image segmentation applications [19,20].…”

Section: Overview Of Ultrasound Imaging Artificial Intelligent Segmen...mentioning

confidence: 99%

Using AI Segmentation Models to Improve Foreign Body Detection and Triage from Ultrasound Images

Holland,

Hernandez Torres,

Snider

2024

Bioengineering

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Medical imaging can be a critical tool for triaging casualties in trauma situations. In remote or military medicine scenarios, triage is essential for identifying how to use limited resources or prioritize evacuation for the most serious cases. Ultrasound imaging, while portable and often available near the point of injury, can only be used for triage if images are properly acquired, interpreted, and objectively triage scored. Here, we detail how AI segmentation models can be used for improving image interpretation and objective triage evaluation for a medical application focused on foreign bodies embedded in tissues at variable distances from critical neurovascular features. Ultrasound images previously collected in a tissue phantom with or without neurovascular features were labeled with ground truth masks. These image sets were used to train two different segmentation AI frameworks: YOLOv7 and U-Net segmentation models. Overall, both approaches were successful in identifying shrapnel in the image set, with U-Net outperforming YOLOv7 for single-class segmentation. Both segmentation models were also evaluated with a more complex image set containing shrapnel, artery, vein, and nerve features. YOLOv7 obtained higher precision scores across multiple classes whereas U-Net achieved higher recall scores. Using each AI model, a triage distance metric was adapted to measure the proximity of shrapnel to the nearest neurovascular feature, with U-Net more closely mirroring the triage distances measured from ground truth labels. Overall, the segmentation AI models were successful in detecting shrapnel in ultrasound images and could allow for improved injury triage in emergency medicine scenarios.

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Section: Overview Of Ultrasound Imaging Artificial Intelligent Segmen...mentioning

confidence: 99%

Using AI Segmentation Models to Improve Foreign Body Detection and Triage from Ultrasound Images

Holland,

Hernandez Torres,

Snider

2024

Bioengineering

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“…The method enhances the performance of the YOLOv7 object detection algorithm by the integration of a Squeeze and Excitation attention block. This integration greatly improves polyp detection with favorable results (Ahmad et al, 2023). Khryashev et al (2023) proposed to solve the problem of a low number of images in the dataset by data augmentation and used the YOLOv8 model for colorectal polyp detection.…”

Section: Related Workmentioning

confidence: 99%

Colorectal image analysis for polyp diagnosis

Zhu,

Wan,

Shao

et al. 2024

Front. Comput. Neurosci.

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Colorectal polyp is an important early manifestation of colorectal cancer, which is significant for the prevention of colorectal cancer. Despite timely detection and manual intervention of colorectal polyps can reduce their chances of becoming cancerous, most existing methods ignore the uncertainties and location problems of polyps, causing a degradation in detection performance. To address these problems, in this paper, we propose a novel colorectal image analysis method for polyp diagnosis via PAM-Net. Specifically, a parallel attention module is designed to enhance the analysis of colorectal polyp images for improving the certainties of polyps. In addition, our method introduces the GWD loss to enhance the accuracy of polyp diagnosis from the perspective of polyp location. Extensive experimental results demonstrate the effectiveness of the proposed method compared with the SOTA baselines. This study enhances the performance of polyp detection accuracy and contributes to polyp detection in clinical medicine.

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KONet: Toward a Weighted Ensemble Learning Model for Knee Osteoporosis Classification

Rasool,

Ahmad,

Sabina

et al. 2024

IEEE Access

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Knee osteoporosis (KOP) is a skeletal disorder characterized by bone tissue degradation and low bone density, leading to a high risk of bone fractures in the knee area. The traditional method for identifying knee osteoporosis is knee radiography, which requires sufficient expertise from specialists. However, the sheer volume of X-rays and the subtle variations among them may lead to misinterpretation. In recent years, deep learning algorithms have revolutionized medical diagnosis and reduced misclassification. Specifically, convolutional neural network (CNN)-based algorithms have been utilized to automate the diagnostic process as they have the inherent ability to extract important features that are difficult to identify manually. However, relying on a single method may result in suboptimal performance, leading to ineffective deployment in the medical domain. To alleviate this issue, in this study, we propose a robust detection method, KONet, which utilizes a weighted ensemble approach to distinguish between normal and osteoporotic knee conditions, even when there are minor variations in the data. To validate the architectural choices in the ensemble approach, we conducted experiments on various state-of-the-art CNN-based models using transfer learning. Extensive experiments indicated that the proposed model achieves a higher accuracy than existing models, outperforming the state-of-the-art models by a significant margin.

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Automated Detection of Gastric Lesions in Endoscopic Images by Leveraging Attention-Based YOLOv7

Cited by 7 publications

References 37 publications

Using AI Segmentation Models to Improve Foreign Body Detection and Triage from Ultrasound Images

Using AI Segmentation Models to Improve Foreign Body Detection and Triage from Ultrasound Images

Colorectal image analysis for polyp diagnosis

KONet: Toward a Weighted Ensemble Learning Model for Knee Osteoporosis Classification

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