Multispectral Biopsy Image Based Colorectal Tumor Grader

Kunhoth, Suchithra; Al-Maadeed, Somaya

doi:10.1007/978-3-319-60964-5_29

Cited by 7 publications

(8 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In addition to using conventional RGB/grayscale images, multispectral images often provide extensive information to support classification tasks. Kunhoth et al [ 88 ] used a multispectral image acquisition system to develop a colorectal biopsy section database divided into training sets and test sets. In order to avoid the deviation, 50 iterations were run, and the results of a single operation were averaged, which finally proved that the database had a high classification accuracy.…”

Section: Use Of Ai In Diagnosis Of Crcmentioning

confidence: 99%

See 1 more Smart Citation

Development of artificial intelligence technology in diagnosis, treatment, and prognosis of colorectal cancer

Liang¹,

Shu²,

Zhang³

et al. 2022

WJGO

View full text Add to dashboard Cite

Artificial intelligence (AI) technology has made leaps and bounds since its invention. AI technology can be subdivided into many technologies such as machine learning and deep learning. The application scope and prospect of different technologies are also totally different. Currently, AI technologies play a pivotal role in the highly complex and wide-ranging medical field, such as medical image recognition, biotechnology, auxiliary diagnosis, drug research and development, and nutrition. Colorectal cancer (CRC) is a common gastrointestinal cancer that has a high mortality, posing a serious threat to human health. Many CRCs are caused by the malignant transformation of colorectal polyps. Therefore, early diagnosis and treatment are crucial to CRC prognosis. The methods of diagnosing CRC are divided into imaging diagnosis, endoscopy, and pathology diagnosis. Treatment methods are divided into endoscopic treatment, surgical treatment, and drug treatment. AI technology is in the weak era and does not have communication capabilities. Therefore, the current AI technology is mainly used for image recognition and auxiliary analysis without in-depth communication with patients. This article reviews the application of AI in the diagnosis, treatment, and prognosis of CRC and provides the prospects for the broader application of AI in CRC.

show abstract

Section: Use Of Ai In Diagnosis Of Crcmentioning

confidence: 99%

“…In order to avoid the deviation, 50 iterations were run, and the results of a single operation were averaged, which finally proved that the database had a high classification accuracy. The colorectal biopsy section database could help diagnose CRC[ 88 ].…”

Section: Use Of Ai In Diagnosis Of Crcmentioning

confidence: 99%

Development of artificial intelligence technology in diagnosis, treatment, and prognosis of colorectal cancer

Liang¹,

Shu²,

Zhang³

et al. 2022

WJGO

View full text Add to dashboard Cite

show abstract

“…In [11,3,4,12], several texture features have been tested for a better representation of colorectal histology images. In [13] The gland segmentation is a pre-requisite step in all the morphological based approaches as well as in region-specific feature-based approaches.…”

Section: Related Workmentioning

confidence: 99%

“…We carried out experiments with three different texture features: local binary pattern (LBP) [22], local phase quantization (LPQ) [23] and binarized statistical image features (BSIF) [24]. LBP and LPQ texture features have been presented as an effective representation of colorectal histology images for classification [21,4,3].…”

Section: Texture Based Featuresmentioning

confidence: 99%

“…In computational pathology, the identification of non-cancerous abnormal tissue is a challenging task due to their similarity with the normal and cancerous tissue. A very few works can be found on the identification of other abnormalities which are non-cancerous [3,4] but in clinical practice, are substantial to locate as they can lead to carcinoma. In this study, we present an autonomous framework where stress is given to the abnormal tissue due to their ability to developing cancer and the need to learn distinctive features to increase the inter-class variability.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Glandular structure-guided classification of microscopic colorectal images using deep learning

Awan

Al-Maadeed

Al-Saady

et al. 2020

Computers & Electrical Engineering

View full text Add to dashboard Cite

In this work, we propose to automate the pre-cancerous tissue abnormality analysis by performing the classification of image patches using a novel two-stage convolutional neural network (CNN) based framework. Rather than training a model with features that may correlate among various classes, we propose to train a model using the features which vary across the different classes. Our framework processes the input image to locate the region of interest (glandular structures) and then feeds the processed image to a classification model for abnormality prediction. Our experiments show that our proposed approach improves the classification performance by up to 7% using CNNs and more than 10% while using texture descriptors. When testing with gland segmented images, our experiments reveal that the performance of our classification approach is dependent on the gland segmentation approach which is a key task in gland structure-guided classification.

show abstract

Hyperplastic and tubular polyp classification using machine learning and feature selection

Doğan,

Akay,

Doğan

et al. 2024

Intelligence-Based Medicine

View full text Add to dashboard Cite

Multispectral Biopsy Image Based Colorectal Tumor Grader

Cited by 7 publications

References 25 publications

Development of artificial intelligence technology in diagnosis, treatment, and prognosis of colorectal cancer

Development of artificial intelligence technology in diagnosis, treatment, and prognosis of colorectal cancer

Glandular structure-guided classification of microscopic colorectal images using deep learning

Hyperplastic and tubular polyp classification using machine learning and feature selection

Contact Info

Product

Resources

About