Interpretative computer-aided lung cancer diagnosis: From radiology analysis to malignancy evaluation

Zheng, Shuang; Shen, Zhiqiang; Pei, Chenhao; Ding, Wangbin; Lin, Haojin; Zheng, Jiepeng; Lin, Pang; Zheng, Bin; Huang, Liqin

doi:10.1016/j.cmpb.2021.106363

Cited by 24 publications

(8 citation statements)

References 20 publications

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“…The DL framework provided an accuracy of 97.27%. Zheng et al [ 49 ] proposed a combination of radiology analysis and malignancy evaluation network (R2MNet) to evaluate pulmonary nodule malignancy by radiology features analysis. In addition, they proposed channel-dependent activation mapping (CDAM) to visualize characteristics and shed light on the decision process of deep neural networks for model explanations (DNN) that obtained an area under the curve (AUC) of 97.52% on nodal radiology analysis.…”

Section: Related Workmentioning

confidence: 99%

Automatic detection and classification of lung cancer CT scans based on deep learning and ebola optimization search algorithm

Mohamed,

Oyelade,

Ezugwu

2023

PLoS ONE

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Recently, research has shown an increased spread of non-communicable diseases such as cancer. Lung cancer diagnosis and detection has become one of the biggest obstacles in recent years. Early lung cancer diagnosis and detection would reliably promote safety and the survival of many lives globally. The precise classification of lung cancer using medical images will help physicians select suitable therapy to reduce cancer mortality. Much work has been carried out in lung cancer detection using CNN. However, lung cancer prediction still becomes difficult due to the multifaceted designs in the CT scan. Moreover, CNN models have challenges that affect their performance, including choosing the optimal architecture, selecting suitable model parameters, and picking the best values for weights and biases. To address the problem of selecting optimal weight and bias combination required for classification of lung cancer in CT images, this study proposes a hybrid metaheuristic and CNN algorithm. We first designed a CNN architecture and then computed the solution vector of the model. The resulting solution vector was passed to the Ebola optimization search algorithm (EOSA) to select the best combination of weights and bias to train the CNN model to handle the classification problem. After thoroughly training the EOSA-CNN hybrid model, we obtained the optimal configuration, which yielded good performance. Experimentation with the publicly accessible Iraq-Oncology Teaching Hospital / National Center for Cancer Diseases (IQ-OTH/NCCD) lung cancer dataset showed that the EOSA metaheuristic algorithm yielded a classification accuracy of 0.9321. Similarly, the performance comparisons of EOSA-CNN with other methods, namely, GA-CNN, LCBO-CNN, MVO-CNN, SBO-CNN, WOA-CNN, and the classical CNN, were also computed and presented. The result showed that EOSA-CNN achieved a specificity of 0.7941, 0.97951, 0.9328, and sensitivity of 0.9038, 0.13333, and 0.9071 for normal, benign, and malignant cases, respectively. This confirms that the hybrid algorithm provides a good solution for the classification of lung cancer.

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Section: Related Workmentioning

confidence: 99%

Automatic detection and classification of lung cancer CT scans based on deep learning and ebola optimization search algorithm

Mohamed,

Oyelade,

Ezugwu

2023

PLoS ONE

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“…Update bypass rider's location. Typical route used by other riders has been avoided by the bypass rider; the bypass rider's location is updated at random and reported in Equation (27).…”

Section: To Update the Rider's Locationmentioning

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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“…The work [14] used DFCNet for classification and data augmentation techniques for improving the classification performance on the LIDC-IDRI dataset with an accuracy of 86 %. The paper [15] used CNN with R2MNet architecture for classification with the LUNA16 dataset and data augmentation technology by scaling, flip, and rotation and obtained an accuracy of 94.74 %. By studying the relevant papers, it was found that there is no static approach for each dataset to address the class imbalance problem, which would be the best to increase the classification efficiency.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Hybrid selection framework for class balancing approaches based on integrated CNN and decision making techniques for lung cancer diagnosis

Jassim

Jaber

2022

EEJET

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Lung cancer is the fastest-growing and most dangerous type of cancer worldwide. It ranks first among cancer diseases in the number of deaths, and diagnosing it at late stages makes treatment more difficult. Artificial intelligence has played an essential role in the medical field in general, and early diagnosis of diseases and analyzing medical images in particular, as it can reduce human errors that may occur with the medical expert in medical image analysis. In this study, a hybrid framework is proposed between deep learning using the proposed convolutional neural network and multi-criteria decision-making techniques in order to reach an effective and accurate classification model for lung cancer diagnosis and select the best methodology to solve the problem of class imbalance datasets, which is a general problem in medical data that causes problems and errors in prediction. The IQ-OTHNCCD dataset that has a class imbalance was used. Three class balancing techniques were used separately and the data from each one enters the proposed convolutional neural network for feature extraction and classification. Then the Fuzzy-Weighted Zero-Inconsistency algorithm and VIKOR were used to make the ranking for the best classification approach and determine the best technique to balance the classes. This contributed to increasing the efficiency of the classification, where the best model got an accuracy of 99.27 %, sensitivity of 99.33 %, specificity of 99 %, precision of 98.67 % and F1-score of 99 %. This study can be applied to any data that suffers from the class imbalance problem to find the best technique that gives the highest classification accuracy.

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Interpretative computer-aided lung cancer diagnosis: From radiology analysis to malignancy evaluation

Cited by 24 publications

References 20 publications

Automatic detection and classification of lung cancer CT scans based on deep learning and ebola optimization search algorithm

Automatic detection and classification of lung cancer CT scans based on deep learning and ebola optimization search algorithm

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

Hybrid selection framework for class balancing approaches based on integrated CNN and decision making techniques for lung cancer diagnosis

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