IAPC2: Improved and Automatic Classification of Polyp for Colorectal Cancer

Shahid, Bisma; Abbas, Maria; Ur Rehman, Abd; Ul Abideen, Zain

doi:10.1109/icbats57792.2023.10111431

Cited by 2 publications

(1 citation statement)

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“…The issue of survival analysis is converted into a semisupervised learning problem using a deep neural network model. Estimating the likelihood of survival at varying times is challenging here [11]. A new objective function for data fitting has been developed in light of the increasing prevalence of data filtering and the flattening survival probability seen in survival analysis.…”

Section: B a Model For A Large-scale Parallelization Of Deep Learning...mentioning

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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Section: B a Model For A Large-scale Parallelization Of Deep Learning...mentioning

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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Optimizing Deep Learning Algorithms for Colorectal Tumor Identification via Noise Removal

Santhoshi,

Muthukumaravel

2024

2024 International Conference on Knowledge Engineering and Communication Systems (ICKECS)

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IAPC2: Improved and Automatic Classification of Polyp for Colorectal Cancer

Cited by 2 publications

References 24 publications

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

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

Optimizing Deep Learning Algorithms for Colorectal Tumor Identification via Noise Removal

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