A Comprehensive Review on Deep Learning Based Lung Nodule Detection in Computed Tomography Images

Nakrani, Mahender G.; Sable, Ganesh S.; Shinde, Ulhas B.

doi:10.1007/978-981-15-5400-1_12

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…However, standard CNN is unsuitable for medical imaging processes with small datasets (hundreds to thousands number of data). A comprehensive review of various types of CNN for pulmonary nodules detection, false positive reduction and classification has been done in [15][16][17][18]. Table 1, on the other hand, focuses on CNN with advanced implementation approaches rather than conventional methods or traditional CNN.…”

Section: Introductionmentioning

confidence: 99%

Lung Nodules Classification Using Convolutional Neural Network with Transfer Learning

Saleh

Rosdi

2023

Lecture Notes on Data Engineering and Communications Technologies

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Healthcare industry plays a vital role in improving daily life. Machine learning and deep neural networks have contributed a lot to benefit various industries nowadays. Agriculture, healthcare, machinery, aviation, management, and even education have all benefited from the development and implementation of machine learning. Deep neural networks provide insight and assistance in improving daily activities. Convolutional neural network (CNN), one of the deep neural network methods, has had a significant impact in the field of computer vision. CNN has long been known for its ability to improve detection and classification in images. With the implementation of deep learning, more deep knowledge can be gathered and help healthcare workers to know more about a patient's disease. Deep neural networks and machine learning are increasingly being used in healthcare. The benefit they provide in terms of improved detection and classification has a positive impact on healthcare. CNNs are widely used in the detection and classification of imaging tasks like CT and MRI scans. Although CNN has advantages in this industry, the algorithm must be trained with a large number of data sets in order to achieve high accuracy and performance. Large medical datasets are always unavailable due to a variety of factors such as ethical concerns, a scarcity of expert explanatory notes and labelled data, and a general scarcity of disease images. In this paper, lung nodules classification using CNN with transfer learning is proposed to help in classifying benign and malignant lung nodules from CT scan images. The objectives of this study are to pre-process lung nodules data, develop a CNN with transfer learning algorithm, and analyse the effectiveness of CNN with transfer learning compared to standard of other methods. According to the findings of this study, CNN with transfer learning outperformed standard CNN without transfer learning.

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Section: Introductionmentioning

confidence: 99%

Lung Nodules Classification Using Convolutional Neural Network with Transfer Learning

Saleh

Rosdi

2023

Lecture Notes on Data Engineering and Communications Technologies

View full text Add to dashboard Cite

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Transfer Learning for Lung Nodules Classification with CNN and Random Forest

Yahya Saleh,

Ka Chin,

Ameera Rosdi

2023

JST

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Machine learning and deep neural networks are improving various industries, including healthcare, which improves daily life. Deep neural networks, including Convolutional Neural Networks (CNNs), provide valuable insights and support in improving daily activities. In particular, CNNs enable the recognition and classification of images from CT and MRI scans and other tasks. However, training a CNN requires many datasets to attain optimal accuracy and performance, which is challenging in the medical field due to ethical worries, the lack of descriptive notes from experts and labeled data, and the overall scarcity of disease images. To overcome these challenges, this work proposes a hybrid CNN with transfer learning and a random forest algorithm for classifying lung cancer and non-cancer from CT scan images. This research aims include preprocessing lung nodular data, developing the proposed algorithm, and comparing its effectiveness with other methods. The findings indicate that the proposed hybrid CNN with transfer learning and random forest performs better than standard CNNs without transfer learning. This research demonstrates the potential of using machine learning algorithms in the healthcare industry, especially in disease detection and classification.

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A Comprehensive Review on Deep Learning Based Lung Nodule Detection in Computed Tomography Images

Cited by 2 publications

References 14 publications

Lung Nodules Classification Using Convolutional Neural Network with Transfer Learning

Lung Nodules Classification Using Convolutional Neural Network with Transfer Learning

Transfer Learning for Lung Nodules Classification with CNN and Random Forest

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