Improving machine learning recognition of colorectal cancer using 3D GLCM applied to different color spaces

Alqudah, Ali Mohammad; Alqudah, Amin

doi:10.1007/s11042-022-11946-9

Cited by 33 publications

(10 citation statements)

References 37 publications

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“…In recent years, a slew of deep learning algorithms has been presented (Alqudah 2020 ; Esteva et al 2021 ; Alqudah et al 2021a , b , c ; Kanavati et al 2020 ; LeCun et al 2015 ). The most frequently used, powerful, and efficient deep learning algorithms are the CNN (Alqudah 2020 ; Alqudah et al 2021a , c ; Alqudah and Alqudah 2022a ) and Long Short-Term Memory (LSTM) (Ozturk and Ozkaya 2020 ; Petmezas et al 2021 ; Cinar and Tuncer 2021 ; Jelodar et al 2020 ). In the following subsections we will discuss in detail the developed separated and hybrid CNN and LSTM models.…”

Section: Methodsmentioning

confidence: 99%

“…The accuracy, sensitivity, precision, and specificity of the data were then determined using these annotations. These indicators show how exactly lung sounds are diagnosed (Alqudah and Alqudah 2022a ). True positive (TP), false positive (FP), false negative (FN), and true negative (TN) are four different sorts of statistical values used to calculate these measures (Kanavati et al 2020 ; Alqudah et al 2021c ).…”

Section: Methodsmentioning

confidence: 99%

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Deep learning models for detecting respiratory pathologies from raw lung auscultation sounds

2022

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Deep learning models for detecting respiratory pathologies from raw lung auscultation sounds

2022

Self Cite

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“…These applications can include the diagnosis of various cancers or types of cardiovascular, liver, respiratory diseases, etc. [38] , [39] , [40] , [41] , [42] , [43] , [44] . The development of artificial intelligence-based solutions such as deep learning has led to the use of medical images in the accurate and rapid diagnosis of various diseases.…”

Section: Background and Related Workmentioning

confidence: 99%

A fuzzy fine-tuned model for COVID-19 diagnosis

Esmi¹,

Golshan²,

Asadi³

et al. 2023

Computers in Biology and Medicine

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“…Alqudah et al [ 94 ] presented an ML-based model for the detection of colon cancer. They utilized 3D GLCM matrices of three different color spaces to extract texture features from the input HIs.…”

Section: Literature Reviewmentioning

confidence: 99%

Colon Cancer Diagnosis Based on Machine Learning and Deep Learning: Modalities and Analysis Techniques

Tharwat

Sakr

El–Sappagh

et al. 2022

Sensors

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The treatment and diagnosis of colon cancer are considered to be social and economic challenges due to the high mortality rates. Every year, around the world, almost half a million people contract cancer, including colon cancer. Determining the grade of colon cancer mainly depends on analyzing the gland’s structure by tissue region, which has led to the existence of various tests for screening that can be utilized to investigate polyp images and colorectal cancer. This article presents a comprehensive survey on the diagnosis of colon cancer. This covers many aspects related to colon cancer, such as its symptoms and grades as well as the available imaging modalities (particularly, histopathology images used for analysis) in addition to common diagnosis systems. Furthermore, the most widely used datasets and performance evaluation metrics are discussed. We provide a comprehensive review of the current studies on colon cancer, classified into deep-learning (DL) and machine-learning (ML) techniques, and we identify their main strengths and limitations. These techniques provide extensive support for identifying the early stages of cancer that lead to early treatment of the disease and produce a lower mortality rate compared with the rate produced after symptoms develop. In addition, these methods can help to prevent colorectal cancer from progressing through the removal of pre-malignant polyps, which can be achieved using screening tests to make the disease easier to diagnose. Finally, the existing challenges and future research directions that open the way for future work in this field are presented.

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Improving machine learning recognition of colorectal cancer using 3D GLCM applied to different color spaces

Cited by 33 publications

References 37 publications

Deep learning models for detecting respiratory pathologies from raw lung auscultation sounds

Deep learning models for detecting respiratory pathologies from raw lung auscultation sounds

A fuzzy fine-tuned model for COVID-19 diagnosis

Colon Cancer Diagnosis Based on Machine Learning and Deep Learning: Modalities and Analysis Techniques

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