Computer Vision Applied in Medical Technology: The Comparison of Image Classification and Object Detection on Medical Images

Yan, Haotian

doi:10.2991/cecs-18.2018.17

Cited by 4 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Scientists are striving to enhance the applicability of computer vision (CV) in medical technology, as per research [34] that evaluated two common algorithms. CNN extracts feature vectors from images, while RCNN integrates CNN and region features for object detection.…”

Section: Medical Trainingmentioning

confidence: 99%

Application of Computer Vision and Mobile Systems in Education: A Systematic Review

Rubaiya Tasnim Sholi,

Sarker,

Md. Salman Sohel

et al. 2024

Int. J. Interact. Mob. Technol.

View full text Add to dashboard Cite

The computer vision industry has experienced a significant surge in growth, resulting in numerous promising breakthroughs in computer intelligence. The present review paper outlines the advantages and potential future implications of utilizing this technology in education. A total of 84 research publications have been thoroughly scrutinized and analyzed. The study revealed that computer vision technology integrated with a mobile application is exceptionally useful in monitoring students’ perceptions and mitigating academic dishonesty. Additionally, it facilitates the digitization of handwritten scripts for plagiarism detection and automates attendance tracking to optimize valuable classroom time. Furthermore, several potential applications of computer vision technology for educational institutions have been proposed to enhance students’ learning processes in various faculties, such as engineering, medical science, and others. Moreover, the technology can also aid in creating a safer campus environment by automatically detecting abnormal activities such as ragging, bullying, and harassment.

show abstract

Section: Medical Trainingmentioning

confidence: 99%

Application of Computer Vision and Mobile Systems in Education: A Systematic Review

Rubaiya Tasnim Sholi,

Sarker,

Md. Salman Sohel

et al. 2024

Int. J. Interact. Mob. Technol.

View full text Add to dashboard Cite

show abstract

“…Yoshimasa et al (Horie, Yoshio et al 2019) employed the CNN to classify the esophageal cancer including squamous cell carcinoma and adenocarcinoma. Recently, CNNs have been proved effective methods for many medical imaging tasks, including feature recognition (Yan 2018), image analysis (Singh, Rote et al 2018), and lesion detection (Tanaka, Fujiwara et al 2018).…”

Section: Related Workmentioning

confidence: 99%

ELNet:Automatic classification and segmentation for esophageal lesions using convolutional neural network

Wen

et al. 2021

Medical Image Analysis

View full text Add to dashboard Cite

Automatic and accurate esophageal lesion classification and segmentation is of great significance to clinically estimate the lesion status of esophageal disease and make suitable diagnostic schemes. Due to individual variations and visual similarities of lesions in shapes, colors and textures, current clinical methods remain subject to potential high-risk and time-consumption issues. In this paper, we propose an Esophageal Lesion Network (ELNet) for automatic esophageal lesion classification and segmentation using deep convolutional neural networks (DCNNs). The underlying method automatically integrates dual-view contextual lesion information to extract global features and local features for esophageal lesion classification of four esophageal image types (Normal, Inflammation, Barrett, and Cancer) and proposes lesion-specific segmentation network for automatic esophageal lesion annotation of three esophageal lesion types at pixel level. For established clinical large-scale database of 1051 white-light endoscopic images, ten-fold cross-validation is used in method validation. Experiment results show that the proposed framework achieves classification with sensitivity of 0.9034, specificity of 0.9718 and accuracy of 0.9628, and the segmentation with sensitivity of 0.8018, specificity of 0.9655 and accuracy of 0.9462. All of these indicate that our method enables an efficient, accurate and reliable esophageal lesion diagnosis in clinical.The main contributions of our work can be generalized as follows: 1 For the first time, proposed ELNet enables an automatically and reliably comprehensive esophageal lesions classification of four esophageal lesion types (Normal, Inflammation, Barrett, and Cancer) and lesion-specific segmentation from clinically white-light esophageal images to make suitable and repaid diagnostic schemes for clinicians. 2 A novel Dual-Stream network (DSN) is proposed for esophageal lesion classification. DSN automatically integrates dual-view contextual lesion information using two CNN streams to complementarily extract the global features from the holistic esophageal images and the local features from the lesion patches. 3 Lesion-specific esophageal lesion annotation with Segmentation Network with Classification (SNC) strategy is proposed to automatically annotate three lesion types (Inflammation, Barrett, Cancer) at pixel level to reduce the intra-class differences of esophageal lesions. 4 A clinically large-scale database esophageal database is established for esophageal lesions classification and segmentation. This database includes 1051 white-light esophageal images, which consists of endoscopic images in four different lesion types. Each image in this database has a classification label and its corresponding segmentation annotation.

show abstract

Impact of Computer Vision With Deep Learning Approach in Medical Imaging Diagnosis

Charleen

Angelica

Purnama

et al. 2021

2021 1st International Conference on Computer Science and Artificial Intelligence (ICCSAI)

View full text Add to dashboard Cite

Computer Vision Applied in Medical Technology: The Comparison of Image Classification and Object Detection on Medical Images

Cited by 4 publications

References 8 publications

Application of Computer Vision and Mobile Systems in Education: A Systematic Review

Application of Computer Vision and Mobile Systems in Education: A Systematic Review

ELNet:Automatic classification and segmentation for esophageal lesions using convolutional neural network

Impact of Computer Vision With Deep Learning Approach in Medical Imaging Diagnosis

Contact Info

Product

Resources

About