A CBR framework with gradient boosting based feature selection for lung cancer subtype classification

Ramos-González, Juan; López-Sánchez, Daniel; Castellanos-Garzón, José A.; Paz, Juan F. De; Corchado, Juan M.

doi:10.1016/j.compbiomed.2017.05.010

Cited by 44 publications

(14 citation statements)

References 31 publications

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“…Using the incremental learning method, this approach stores new cases in the cases database for future issues. Such features are the main reason for the difference between CBR and other methods of artificial intelligence and result in the increasing application of this approach in different fields of science (Ramos‐González, López‐Sánchez, Castellanos‐Garzón, de Paz, & Corchado, ). Case‐oriented approaches in the field of medical diagnosis are very effective, because case‐based reasoning is in accordance with the usual decision‐making process of physicians.…”

Section: Discussionmentioning

confidence: 99%

Developing a new intelligent system for the diagnosis of oral medicine with case‐based reasoning approach

et al. 2019

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Objective Since the clinical manifestations of many oral diseases can be quite similar despite the wide variety in etiology and pathology, the differential diagnosis of oral diseases is a complex and challenging process. Intelligent system for differential diagnosis of oral medicine using the artificial intelligence (AI) capabilities helps specialists in achieving differential diagnosis in a wide range of oral diseases. Materials and Methods First, the essential data elements to design and develop an intelligent system were identified in a cross‐sectional descriptive study. The case‐based reasoning method was selected to design and implement the system, which consists of three stages: collect the clinical data, construct the cases database, and case‐based reasoning cycle. The problem is solved by CBR method in a cycle consisting of four main stages of retrieval, reuse, review, and retention. The evaluation process was conducted in a pilot‐based way through the evaluation of the system's performance in the clinical setting and also using the usability assessment questionnaire. Results The output of the present project is a web‐based intelligent information system, which is developed using the Visual Studio 2015 software. The database of this system is the Microsoft SQL Server version 2012, which has been programmed based on Net framework (version 4.5 or higher) using Visual Basic language. The results of the system evaluation by specialists in clinical settings showed that the system's diagnosis power in different aspects of the disease is influenced by their prevalence and incidence. Conclusions System development using the artificial intelligence capabilities and through the clinical data analysis has potential to help specialists to determine the best diagnostic strategy to achieve a differential diagnosis of a wide range of oral diseases. The results of evaluation present the potential of the system to improve the quality and efficiency of patient care.

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Section: Discussionmentioning

confidence: 99%

Developing a new intelligent system for the diagnosis of oral medicine with case‐based reasoning approach

et al. 2019

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“…Ramos‐González et al (2017) by using a similar training procedure for every CNN model optimizes the parameters with a batch size of 32, and it achieves 70% accuracy. Yang et al (2020) proposed a deep learning model for detecting lung cancer using UNet and CapNet for both lower and higher information in nodules, achieves 84% accuracy.…”

Section: Cnn Structure and Fusion Methodsmentioning

confidence: 99%

A study on specific learning algorithms pertaining to classify lung cancer disease

et al. 2021

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Lung cancer is a worldwide precarious disease and it is encouraged by the abnormal growth of cells in bronchi. Spotting the cancer cells is unknown until it leads to respiration issues and the muddling of organs working. Due to problems, limited or incorrect selection of hypothesis space, and dropping into local minima, single learners often give erratic output in an existing approach. The ensemble method accomplished a dataset that is free and composed of computed tomography (CT) images. The annotation process reveals observed lung lesions and provides a degree of malignancy for each lesion. Detection of benign and malignant nodules is recognized using deep convolutional frameworks AlexNet, SqueezeNet, GoogleNet, ResNet, and Inception ResNet, achieves higher accuracy (93%) than other convolutional neural networks (CNNs). Eight machine learning methods are involved for achieving better performance. The prediction probability obtained from CNN is applied as input to support vector machines (SVM), K‐nearest neighbours (KNN), naive Bayes (NB), multi‐layer perceptron (MLP), decision trees (DT), gradient boosted regression trees (GBRT), and adaptive boosting. The composition of GoogleNet model and AdaBoost classifier reached the most coherent classification accuracy as 99%. This is one of the best ways to analyse early detection and it increases the survival rate. Therefore, the result from the proposed deep CNN and ML technique achieves better precision than sputum cytology, X‐Ray process, and earlier detection of lung cancer.

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“…Four classifiers are used to test the proposed model namely Backpropagation Neural Network, SVM, ELM and Regularized Extreme Learning Machine. Ramos-Gonzalez et al [19] proposed the application of supervised machine learning for classification of several types of cancer via deep learning. The MLP neural network architecture is used for lung cancer classification.…”

Section: Related Workmentioning

confidence: 99%

Ensemble learning‐based classification of microarray cancer data on tree‐based features

Dagnew

Shekar

2021

Cognitive Comp and Systems

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Cancer is a group of related diseases with high mortality rate characterized by abnormal cell growth which attacks the body tissues. Microarray cancer data is a prominent research topic across many disciplines focused to address problems related to the higher curse of dimensionality, a small number of samples, noisy data and imbalance class. A random forest (RF) tree‐based feature selection and ensemble learning based on hard voting and soft voting is proposed to classify microarray cancer data using six different base classifiers. The selected features due to RF tree are submitted to the base classifiers as the training set. Then, an ensemble learning method is applied to the base classifiers in which case each base classifier predicts class label individually. The final prediction is carried out hard and soft voting techniques that use majority voting and weighted probability on the test set. The proposed ensemble learning method is validated on eight different standard microarray cancer datasets, of which three of the datasets are binary class and the remaining five datasets are multi‐class datasets. Experimental results of the proposed method show 1.00 classification accuracy on six of the datasets and 0.96 on two of the datasets.

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A CBR framework with gradient boosting based feature selection for lung cancer subtype classification

Cited by 44 publications

References 31 publications

Developing a new intelligent system for the diagnosis of oral medicine with case‐based reasoning approach

Developing a new intelligent system for the diagnosis of oral medicine with case‐based reasoning approach

A study on specific learning algorithms pertaining to classify lung cancer disease

Ensemble learning‐based classification of microarray cancer data on tree‐based features

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