Automated classification of protein subcellular localization in immunohistochemistry images to reveal biomarkers in colon cancer

Xue, Zhen‐Zhen; Wu, Yanxia; Gao, Qingzu; Zhao, Liang; Xu, Ying-Ying

doi:10.1186/s12859-020-03731-y

Cited by 27 publications

(31 citation statements)

References 38 publications

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“…Moreover, massive computational challenges, such as high dimensionality, small sample sizes, high noise, and unbalanced categories, introduce difficulties in the analysis and processing of cancer gene data. Therefore, various powerful methods have been proposed by researchers to address these problems [3].…”

Section: Introductionmentioning

confidence: 99%

Cancer Classification with a Cost-Sensitive Naive Bayes Stacking Ensemble

Xiong

2021

Computational and Mathematical Methods in Medicine

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Ensemble learning combines multiple learners to perform combinatorial learning, which has advantages of good flexibility and higher generalization performance. To achieve higher quality cancer classification, in this study, the fast correlation-based feature selection (FCBF) method was used to preprocess the data to eliminate irrelevant and redundant features. Then, the classification was carried out in the stacking ensemble learner. A library for support vector machine (LIBSVM), K -nearest neighbor (KNN), decision tree C4.5 (C4.5), and random forest (RF) were used as the primary learners of the stacking ensemble. Given the imbalanced characteristics of cancer gene expression data, the embedding cost-sensitive naive Bayes was used as the metalearner of the stacking ensemble, which was represented as CSNB stacking. The proposed CSNB stacking method was applied to nine cancer datasets to further verify the classification performance of the model. Compared with other classification methods, such as single classifier algorithms and ensemble algorithms, the experimental results showed the effectiveness and robustness of the proposed method in processing different types of cancer data. This method may therefore help guide cancer diagnosis and research.

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Section: Introductionmentioning

confidence: 99%

Cancer Classification with a Cost-Sensitive Naive Bayes Stacking Ensemble

Xiong

2021

Computational and Mathematical Methods in Medicine

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“…The method used by these authors obtained models with accuracies of 84%. [35] Masud et al use the LC25000 dataset and CNN for CRC and lung cancer detection. The Wavelet Transform is used in their approach, a mathematical function can transform a continuous time signal into different scale components.…”

Section: B Ai Diagnosis For Colorectal Cancermentioning

confidence: 99%

Cancer Diagnosis With the Aid of Artificial Intelligence Modeling Tools

et al. 2022

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Cancer is one of the deadliest diseases in the present days. Its survivability is mostly corelated to early detection and treatment, which means that it is of utmost importance to successfully diagnose the patients. Unfortunately, even with years of experience human errors can happen which leads to the death of many individuals being misdiagnosed. Throughout the years there have been several applications created which could possibly aid doctors in the diagnosis. Neural Networks have always been a powerful tool which can be used in different applications that require an accurate model and the complexity of these models exceeds a human's computational capabilities. In image processing for example, a convolutional neural network can analyze each particular pixel and determine through the convolution function the common properties of different pictures. The objective of this study is to analyze different types of cancer diagnosing methods that have been developed and tested using image processing methods. The analyzed factors are training parameters, image processing technique and the obtained performances. This survey/review can be of significant value to researchers and professionals in medicine and computer science, highlighting areas where there are opportunities to make significant new contributions. INDEX TERMSCancer diagnosis, Image processing, Neural networks I.

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“…Recently, robust and automatic medical image analysis approaches that can deal with these issues were in high demand to enhance the clinical process from diagnosis, the stratification of patients, treatment preparation, intervention, and follow-up [ 11 , 17 , 18 ]. Researchers are attempting to overcome these challenges in order to find the optimal methods for medical image analysis.…”

Section: Deep Neural Network (Dnns)mentioning

confidence: 99%

Deep Learning Approaches for Automatic Localization in Medical Images

Alaskar

Hussain

ِAlmaslukh

et al. 2022

Computational Intelligence and Neuroscience

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Recent revolutionary advances in deep learning (DL) have fueled several breakthrough achievements in various complicated computer vision tasks. The remarkable successes and achievements started in 2012 when deep learning neural networks (DNNs) outperformed the shallow machine learning models on a number of significant benchmarks. Significant advances were made in computer vision by conducting very complex image interpretation tasks with outstanding accuracy. These achievements have shown great promise in a wide variety of fields, especially in medical image analysis by creating opportunities to diagnose and treat diseases earlier. In recent years, the application of the DNN for object localization has gained the attention of researchers due to its success over conventional methods, especially in object localization. As this has become a very broad and rapidly growing field, this study presents a short review of DNN implementation for medical images and validates its efficacy on benchmarks. This study presents the first review that focuses on object localization using the DNN in medical images. The key aim of this study was to summarize the recent studies based on the DNN for medical image localization and to highlight the research gaps that can provide worthwhile ideas to shape future research related to object localization tasks. It starts with an overview on the importance of medical image analysis and existing technology in this space. The discussion then proceeds to the dominant DNN utilized in the current literature. Finally, we conclude by discussing the challenges associated with the application of the DNN for medical image localization which can drive further studies in identifying potential future developments in the relevant field of study.

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Automated classification of protein subcellular localization in immunohistochemistry images to reveal biomarkers in colon cancer

Cited by 27 publications

References 38 publications

Cancer Classification with a Cost-Sensitive Naive Bayes Stacking Ensemble

Cancer Classification with a Cost-Sensitive Naive Bayes Stacking Ensemble

Cancer Diagnosis With the Aid of Artificial Intelligence Modeling Tools

Deep Learning Approaches for Automatic Localization in Medical Images

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