Histopathological Classification of Colorectal Polyps using Deep Learning

Paing, May Phu; Cho, One-Sun; Cho, Jae-Wan

doi:10.1109/icoin56518.2023.10048925

Cited by 2 publications

(2 citation statements)

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“…Deep Learning applications have received significant traction due to their extensive research and development. Deep Learning-based methods have been applied in many tasks, such as classification [9], object detection [10], object tracking [11], and healthcare [12][13][14]. The objective of parking time violation tracking is to identify parking time violations by vehicles, especially in mini-marts which have a limited parking time.…”

Section: Related Workmentioning

confidence: 99%

Parking Time Violation Tracking Using YOLOv8 and Tracking Algorithms

Sharma,

Baral,

Paing

et al. 2023

Sensors

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The major problem in Thailand related to parking is time violation. Vehicles are not allowed to park for more than a specified amount of time. Implementation of closed-circuit television (CCTV) surveillance cameras along with human labor is the present remedy. However, this paper presents an approach that can introduce a low-cost time violation tracking system using CCTV, Deep Learning models, and object tracking algorithms. This approach is fairly new because of its appliance of the SOTA detection technique, object tracking approach, and time boundary implementations. YOLOv8, along with the DeepSORT/OC-SORT algorithm, is utilized for the detection and tracking that allows us to set a timer and track the time violation. Using the same apparatus along with Deep Learning models and algorithms has produced a better system with better performance. The performance of both tracking algorithms was well depicted in the results, obtaining MOTA scores of (1.0, 1.0, 0.96, 0.90) and (1, 0.76, 0.90, 0.83) in four different surveillance data for DeepSORT and OC-SORT, respectively.

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Section: Related Workmentioning

confidence: 99%

Parking Time Violation Tracking Using YOLOv8 and Tracking Algorithms

Sharma,

Baral,

Paing

et al. 2023

Sensors

Self Cite

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“…This method departs from the conventional approach of supervised learning, but it places a focus on the usefulness of hybrid models and the opportunities presented by using unlabeled data. In order to demonstrate the accuracy with which machine learning models can differentiate benign from malignant forms, M. P. Paing et al [6] used deep learning to identify colorectal polyps. This serves as an illustration of the precision with which machine learning models can make this distinction.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning for Economical Colorectal Cancer Diagnosis: Harnessing the Power of Composite Networks with Unsupervised Learning

Ayushi Lowanshi, Nitesh Rastogi

2023

tjjpt

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As a major worldwide health issue, diagnosing colorectal cancer quickly and accurately is essential. The potential of deep learning to aid in colorectal cancer diagnosis has grown substantially in recent years. However, traditional deep learning models are frequently not widely adopted in clinical settings because to their high computational cost and resource intensiveness. To this end, we present "Deep Learning for Economical Colorectal Cancer Diagnosis: Harnessing the Power of Composite Networks with Unsupervised Learning," a novel method for analysing data in this area. The foundation of this study is the idea of using composite networks, which cleverly incorporates unsupervised learning to greatly lessen the training cost. Our algorithm effectively extracts relevant characteristics by exploiting vast volumes of unlabeled data and then fine-tuning on a smaller labelled dataset for targeted diagnostic tasks. The result is a cheap model that doesn't skimp on diagnostic precision. The empirical data we present here show that our composite network achieves high levels of accuracy while using a minimal amount of resources, making it a promising candidate for fast and cheap diagnosis of colorectal cancer. The results presented here highlight the potential of unsupervised learning to make deep learning more accessible and inexpensive for healthcare institutions around the world, and hence have far-reaching implications for the future of medical diagnosis.

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Histopathological Classification of Colorectal Polyps using Deep Learning

Cited by 2 publications

References 16 publications

Parking Time Violation Tracking Using YOLOv8 and Tracking Algorithms

Parking Time Violation Tracking Using YOLOv8 and Tracking Algorithms

Deep Learning for Economical Colorectal Cancer Diagnosis: Harnessing the Power of Composite Networks with Unsupervised Learning

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