Deep learning to find colorectal polyps in colonoscopy: A systematic literature review

Sánchez-Peralta, Luisa F.; Bote-Curiel, Luis; Picón, Artzai; Sánchez‐Margallo, Francisco M.; Pagador, J. Blas

doi:10.1016/j.artmed.2020.101923

Cited by 113 publications

(66 citation statements)

References 87 publications

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“…As discussed before, we do not have a guaranteed treatment for cancer yet, which means the earlier a person is diagnosed, the more time doctors have to devise a treatment plan for him/her, and the patient stands a better chance to beat the disease. Early diagnosis and proper treatment are currently the only way to reduce the number of deaths due to cancer [7]. However, most of the population is deprived of proper diagnostic facilities, which makes the battle against this virulent disease even more unamenable.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach to Diagnosing Lung and Colon Cancer Using a Deep Learning-Based Classification Framework

Masud

Sikder

Nahid

et al. 2021

Sensors

257

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The field of Medicine and Healthcare has attained revolutionary advancements in the last forty years. Within this period, the actual reasons behind numerous diseases were unveiled, novel diagnostic methods were designed, and new medicines were developed. Even after all these achievements, diseases like cancer continue to haunt us since we are still vulnerable to them. Cancer is the second leading cause of death globally; about one in every six people die suffering from it. Among many types of cancers, the lung and colon variants are the most common and deadliest ones. Together, they account for more than 25% of all cancer cases. However, identifying the disease at an early stage significantly improves the chances of survival. Cancer diagnosis can be automated by using the potential of Artificial Intelligence (AI), which allows us to assess more cases in less time and cost. With the help of modern Deep Learning (DL) and Digital Image Processing (DIP) techniques, this paper inscribes a classification framework to differentiate among five types of lung and colon tissues (two benign and three malignant) by analyzing their histopathological images. The acquired results show that the proposed framework can identify cancer tissues with a maximum of 96.33% accuracy. Implementation of this model will help medical professionals to develop an automatic and reliable system capable of identifying various types of lung and colon cancers.

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Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach to Diagnosing Lung and Colon Cancer Using a Deep Learning-Based Classification Framework

Masud

Sikder

Nahid

et al. 2021

Sensors

257

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“…Convolutional neural networks (CNN) [28,29] have surpassed classical machine learning methods [30,31], and even medical expert capabilities [32][33][34]. They have been also successfully applied in colon cancer histopathological classification [35,36], MPM classification [37], polyp detection on colonoscopy [38][39][40], or histological colon tissue staining [41].…”

Section: Introductionmentioning

confidence: 99%

Characterization of Optical Coherence Tomography Images for Colon Lesion Differentiation under Deep Learning

et al. 2021

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(1) Background: Clinicians demand new tools for early diagnosis and improved detection of colon lesions that are vital for patient prognosis. Optical coherence tomography (OCT) allows microscopical inspection of tissue and might serve as an optical biopsy method that could lead to in-situ diagnosis and treatment decisions; (2) Methods: A database of murine (rat) healthy, hyperplastic and neoplastic colonic samples with more than 94,000 images was acquired. A methodology that includes a data augmentation processing strategy and a deep learning model for automatic classification (benign vs. malignant) of OCT images is presented and validated over this dataset. Comparative evaluation is performed both over individual B-scan images and C-scan volumes; (3) Results: A model was trained and evaluated with the proposed methodology using six different data splits to present statistically significant results. Considering this, 0.9695 (±0.0141) sensitivity and 0.8094 (±0.1524) specificity were obtained when diagnosis was performed over B-scan images. On the other hand, 0.9821 (±0.0197) sensitivity and 0.7865 (±0.205) specificity were achieved when diagnosis was made considering all the images in the whole C-scan volume; (4) Conclusions: The proposed methodology based on deep learning showed great potential for the automatic characterization of colon polyps and future development of the optical biopsy paradigm.

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“…In the field of gastroenterology, AI applications in capsule endoscopy[ 53 - 56 ] and in the detection, localization and segmentation of colonic polyps have been reported as well[ 57 - 59 ]. In particular, in the late 2010s, there was an explosion of interest in GC.…”

Section: Introductionmentioning

confidence: 99%

Application of artificial intelligence-driven endoscopic screening and diagnosis of gastric cancer

Hsiao¹,

Wen²,

Lai³

et al. 2021

WJG

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The landscape of gastrointestinal endoscopy continues to evolve as new technologies and techniques become available. The advent of image-enhanced and magnifying endoscopies has highlighted the step toward perfecting endoscopic screening and diagnosis of gastric lesions. Simultaneously, with the development of convolutional neural network, artificial intelligence (AI) has made unprecedented breakthroughs in medical imaging, including the ongoing trials of computer-aided detection of colorectal polyps and gastrointestinal bleeding. In the past demi-decade, applications of AI systems in gastric cancer have also emerged. With AI’s efficient computational power and learning capacities, endoscopists can improve their diagnostic accuracies and avoid the missing or mischaracterization of gastric neoplastic changes. So far, several AI systems that incorporated both traditional and novel endoscopy technologies have been developed for various purposes, with most systems achieving an accuracy of more than 80%. However, their feasibility, effectiveness, and safety in clinical practice remain to be seen as there have been no clinical trials yet. Nonetheless, AI-assisted endoscopies shed light on more accurate and sensitive ways for early detection, treatment guidance and prognosis prediction of gastric lesions. This review summarizes the current status of various AI applications in gastric cancer and pinpoints directions for future research and clinical practice implementation from a clinical perspective.

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Deep learning to find colorectal polyps in colonoscopy: A systematic literature review

Cited by 113 publications

References 87 publications

A Machine Learning Approach to Diagnosing Lung and Colon Cancer Using a Deep Learning-Based Classification Framework

A Machine Learning Approach to Diagnosing Lung and Colon Cancer Using a Deep Learning-Based Classification Framework

Characterization of Optical Coherence Tomography Images for Colon Lesion Differentiation under Deep Learning

Application of artificial intelligence-driven endoscopic screening and diagnosis of gastric cancer

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