An Efficient Combination of Convolutional Neural Network and LightGBM Algorithm for Lung Cancer Histopathology Classification

Hamed, Esraa A.-R.; Salem, Mohammed A.-M.; Badr, Nagwa L.; Tolba, Mohamed F.

doi:10.3390/diagnostics13152469

Cited by 17 publications

(8 citation statements)

References 34 publications

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“…A CNN model achieved accurate identification of lung cancer images, with training and validation accuracy reaching 96.11 percent and 97.2 percent, respectively, while utilizing cross-entropy as the loss function [10]. Among all the models developed in our study [11], the proposed CNN-LightGBM model demonstrates the highest accuracy while utilizing the fewest total parameters. This model comprises merely four convolutional layers, four maximum pooling layers, and one leaky layer.…”

Section: Related Workmentioning

confidence: 85%

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Large-scale Histopathological Colon Cancer Annotation Model Using Machine Learning Techniques

Hamed,

Tolba,

Badr

et al. 2023

IJICIS

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Colon cancer ranks among the leading factors contributing to mortality and morbidity among adults. One of the main components in determining the kind of cancer is the histopathological diagnosis. This study presents the development of a computer-aided diagnosis system for adenocarcinomas of the colon using machine learning (ML) to analyze digital pathology images. A dataset of 10,000 images was gathered from the LC25000 collection, with 5000 images for each class. The Convolutional Neural Network with a Light Gradient Boosting Machine (CNN-LightGBM) with multiple threads was used as the classification model, and the system was evaluated against other ML algorithms. The reported diagnosis accuracy for colon cancer has achieved greater than 90%, outperforming the latest ML algorithms in disease classification accuracy. However, the accuracy was less than that for lung cancer classification based on this approach. This study demonstrates the potential for ML to improve the accuracy and efficiency of medical diagnosis and highlights the need for further research to improve the accuracy of colon cancer diagnosis.

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Section: Related Workmentioning

confidence: 85%

“…In this section, we present a detailed description of the proposed CNN feature extraction model. We carefully examined the pre-trained CNNs, such as AlexNet, VGG, etc., and created our own CNN model with fewer number of parameters to accelerate the processing time as we had previously identified [11].…”

Section: The Proposed Cnn Feature Extraction Modelmentioning

confidence: 99%

Large-scale Histopathological Colon Cancer Annotation Model Using Machine Learning Techniques

Hamed,

Tolba,

Badr

et al. 2023

IJICIS

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“…This section discusses the proposed algorithm for the detection and identification of lung cancer. The algorithm used is the LightGBM algorithm developed by Microsoft [13]. High accuracy rate was obtained with LightGBM algorithm.…”

Section: Methodsmentioning

confidence: 99%

An Explainable Artificial Intelligence Based Early Lung Cancer Risk Prediction Using LightGBM

2023

IJANSER

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Lung cancer is the most common cancer in the world and is also the deadliest. Early diagnosis can improve patients' life expectancy and reduce the cost of treatment. The aim of this study is to help physicians and patients by using explainable artificial intelligence methods to early diagnose risk of lung cancer. This study will create an opportunity for physicians to make early diagnosis and treatment strategies for patients. In this study, a machine learning model was developed to predict the risk of lung cancer by using the LightGBM algorithm. Furthermore, the SHAP method is used to explain why and how the model's predictions are made, thus making the AI model reliable. These explanations increase the acceptability and reliability of the predictions made by model, while helping physicians and patients understand the model's decisions. The results obtained show that the developed LightGBM model can predict the risk of lung cancer with a 100% accuracy rate. The model has achieved great results in terms of accuracy rate and sensitivity. In addition, the SHAP analyses explain which features each of the model's predictions is based on, which will increase the confidence of physicians and patients in the decisions of artificial intelligence.

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“…LightGBM is a distributed and high-performance algorithm designed for gradient-boosting decision trees, specifically based on the Histogram algorithm, characterized by efficiency, speed, and high accuracy. Principle of LightGBM is to iteratively train multiple decision trees and train the next tree based on the results of the previous tree to minimize the loss function ( 12 , 13 ). Combining the RF and LightGBM models can yield more comprehensive and accurate results in research.…”

Section: Introductionmentioning

confidence: 99%

Construction of diagnostic models for the progression of hepatocellular carcinoma using machine learning

Jiang,

Zhou,

Jiang

et al. 2024

Front. Oncol.

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Liver cancer is one of the most prevalent forms of cancer worldwide. A significant proportion of patients with hepatocellular carcinoma (HCC) are diagnosed at advanced stages, leading to unfavorable treatment outcomes. Generally, the development of HCC occurs in distinct stages. However, the diagnostic and intervention markers for each stage remain unclear. Therefore, there is an urgent need to explore precise grading methods for HCC. Machine learning has emerged as an effective technique for studying precise tumor diagnosis. In this research, we employed random forest and LightGBM machine learning algorithms for the first time to construct diagnostic models for HCC at various stages of progression. We categorized 118 samples from GSE114564 into three groups: normal liver, precancerous lesion (including chronic hepatitis, liver cirrhosis, dysplastic nodule), and HCC (including early stage HCC and advanced HCC). The LightGBM model exhibited outstanding performance (accuracy = 0.96, precision = 0.96, recall = 0.96, F1-score = 0.95). Similarly, the random forest model also demonstrated good performance (accuracy = 0.83, precision = 0.83, recall = 0.83, F1-score = 0.83). When the progression of HCC was categorized into the most refined six stages: normal liver, chronic hepatitis, liver cirrhosis, dysplastic nodule, early stage HCC, and advanced HCC, the diagnostic model still exhibited high efficacy. Among them, the LightGBM model exhibited good performance (accuracy = 0.71, precision = 0.71, recall = 0.71, F1-score = 0.72). Also, performance of the LightGBM model was superior to that of the random forest model. Overall, we have constructed a diagnostic model for the progression of HCC and identified potential diagnostic characteristic gene for the progression of HCC.

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An Efficient Combination of Convolutional Neural Network and LightGBM Algorithm for Lung Cancer Histopathology Classification

Cited by 17 publications

References 34 publications

Large-scale Histopathological Colon Cancer Annotation Model Using Machine Learning Techniques

Large-scale Histopathological Colon Cancer Annotation Model Using Machine Learning Techniques

An Explainable Artificial Intelligence Based Early Lung Cancer Risk Prediction Using LightGBM

Construction of diagnostic models for the progression of hepatocellular carcinoma using machine learning

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