Lung and colon cancer classification using medical imaging: a feature engineering approach

Chehade, Aya Hage; Abdallah, Nassib; Marion, Jean-Marie; Oueidat, Mohamad; Chauvet, Pierre

doi:10.1007/s13246-022-01139-x

Cited by 72 publications

(20 citation statements)

References 25 publications

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“…In cancer, machine learning has been used to explore survival and prognosis prediction models for pancreatic, bladder, advanced nasopharyngeal and breast cancers [ 14 – 16 ]. Among these, XGBoost models have been applied to identify lung cancer, colon cancer subtypes [ 17 ], prediction of lung metastases from thyroid cancer [ 18 ] and risk models for identifying lung cancer [ 19 ], with all performing at a high level. In the last decade, nomograms have been considered a reliable method for predicting tumour prognosis [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

Construction of the XGBoost model for early lung cancer prediction based on metabolic indices

Guan

et al. 2023

BMC Med Inform Decis Mak

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Background Lung cancer is a malignant tumour, and early diagnosis has been shown to improve the survival rate of lung cancer patients. In this study, we assessed the use of plasma metabolites as biomarkers for lung cancer diagnosis. In this work, we used a novel interdisciplinary mechanism, applied for the first time to lung cancer, to detect biomarkers for early lung cancer diagnosis by combining metabolomics and machine learning approaches. Results In total, 478 lung cancer patients and 370 subjects with benign lung nodules were enrolled from a hospital in Dalian, Liaoning Province. We selected 47 serum amino acid and carnitine indicators from targeted metabolomics studies using LC‒MS/MS and age and sex demographic indicators of the subjects. After screening by a stepwise regression algorithm, 16 metrics were included. The XGBoost model in the machine learning algorithm showed superior predictive power (AUC = 0.81, accuracy = 75.29%, sensitivity = 74%), with the metabolic biomarkers ornithine and palmitoylcarnitine being potential biomarkers to screen for lung cancer. The machine learning model XGBoost is proposed as an tool for early lung cancer prediction. This study provides strong support for the feasibility of blood-based screening for metabolites and provide a safer, faster and more accurate tool for early diagnosis of lung cancer. Conclusions This study proposes an interdisciplinary approach combining metabolomics with a machine learning model (XGBoost) to predict early the occurrence of lung cancer. The metabolic biomarkers ornithine and palmitoylcarnitine showed significant power for early lung cancer diagnosis.

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Section: Introductionmentioning

confidence: 99%

Construction of the XGBoost model for early lung cancer prediction based on metabolic indices

Guan

et al. 2023

BMC Med Inform Decis Mak

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“…Chehade et al [ 93 ] presented a CAD system that classifies HIs of colon and lung cancer into five different classes. In this model, HIs were preprocessed using image preprocessing techniques, and then the discriminative features were extracted.…”

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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“…of riders present in every group is used to calculate the no. of riders competing in the race, as indicated in Equation (23).…”

Section: Initialize the Rider Group And Its Parametersmentioning

confidence: 99%

Lung and colon cancer classification using hybrid principle component analysis network‐extreme learning machine

Raju

Rao

2022

Concurrency and Computation

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Lung and colon cancers are dangerous diseases that can grow in organs and create a negative impact on human life in certain cases. The histological detection of such malignancies is one of the most critical parts of optimal treatment. As a result, the important objective of this article is to create an effective computerized diagnosis system for identifying adenocarcinomas of the colon as well as, adenocarcinomas and squamous cell carcinomas of the lungs using digital histopathology images and the combination of deep and machine learning techniques. For this, an effective optimized hybrid deep and machine learning framework is developed. This framework consists of two stages. In the first stage, the features of lung and colon images are extracted by principle component analysis network. Then the effective classification is conducted based on extreme learning machine (ELM) with the rider optimization algorithm which classifies lung and colon cancer into five types. The empirical investigation shows that the classification results on the benchmark LC25000 dataset have improved significantly. The use of this model will aid medical professionals in the development of an automatic and reliable system for detecting various forms of lung and colon cancers.

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Lung and colon cancer classification using medical imaging: a feature engineering approach

Cited by 72 publications

References 25 publications

Construction of the XGBoost model for early lung cancer prediction based on metabolic indices

Construction of the XGBoost model for early lung cancer prediction based on metabolic indices

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

Lung and colon cancer classification using hybrid principle component analysis network‐extreme learning machine

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