Superpixel with nanoscale imaging and boosted deep convolutional neural network concept for lung tumor classification

Rani, K. Vijila; Jawhar, S. Joseph

doi:10.1002/ima.22422

Cited by 21 publications

(8 citation statements)

References 27 publications

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“…Different feature extraction methods were performed to extract information from the segmented CT images and proposed system achieved 97.18% of accuracy. Rani et al [21] introduced a method for identification of lung cancer nodules from CT images. Histogram equalization and tophat technique was applied to remove noise from CT images.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Lung Cancer Detection Using Modified AlexNet Architecture and Support Vector Machine

Naseer¹,

Masood²,

Akram³

et al. 2023

Computers, Materials &Amp; Continua

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Lung cancer is the most dangerous and death-causing disease indicated by the presence of pulmonary nodules in the lung. It is mostly caused by the instinctive growth of cells in the lung. Lung nodule detection has a significant role in detecting and screening lung cancer in Computed tomography (CT) scan images. Early detection plays an important role in the survival rate and treatment of lung cancer patients. Moreover, pulmonary nodule classification techniques based on the convolutional neural network can be used for the accurate and efficient detection of lung cancer. This work proposed an automatic nodule detection method in CT images based on modified AlexNet architecture and Support vector machine (SVM) algorithm namely LungNet-SVM. The proposed model consists of seven convolutional layers, three pooling layers, and two fully connected layers used to extract features. Support vector machine classifier is applied for the binary classification of nodules into benign and malignant. The experimental analysis is performed by using the publicly available benchmark dataset Lung nodule analysis 2016 (LUNA16). The proposed model has achieved 97.64% of accuracy, 96.37% of sensitivity, and 99.08% of specificity. A comparative analysis has been carried out between the proposed LungNet-SVM model and existing stateof-the-art approaches for the classification of lung cancer. The experimental results indicate that the proposed LungNet-SVM model achieved remarkable performance on a LUNA16 dataset in terms of accuracy.

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Section: Related Workmentioning

confidence: 99%

“…[17,18,28] requires to improve the detection accuracy. The study [21,23] was based on an imbalanced dataset while other research works [19,20,26,30,31] are based on a complex model.…”

Section: Related Workmentioning

confidence: 99%

Lung Cancer Detection Using Modified AlexNet Architecture and Support Vector Machine

Naseer¹,

Masood²,

Akram³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…A boosted deep CNN concept for lung tumor classification was introduced by Rani et al [12] For segmentation purposes, the Advance Target Map Superpixel-based Region was suggested. The tumor region was then quantified using the nanoimaging theory.…”

Section: Literature Surveymentioning

confidence: 99%

Triple SVM Integrated with Enhanced Random Region Segmentation for Classification of Lung Tumors

A¹,

Jayachandran²

2022

IJACSA

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The rapid growth of Computer vision and Machine Learning applications, especially in Health care systems, assures a secure, innovative lifestyle for society. The implication of these technologies in the early diagnosis of lung tumors helps in lung cancer detection and promises the survival rate of patients. The existing general diagnosis method of lung radiotherapy, i.e., Computed Tomography imaging (CT), doesn't spot exactly affected parts during injuries on lung malignancy. Herein, we propose a computer vision-based diagnostic method empowered with machine learning algorithms to detect lung tumors. The primary objective of the proposed method is to develop an efficient segmentation method to enhance the classification accuracy of lung tumors by implementing a Triple Support Vector Machine (SVM) for the classification of data samples into normal, malignant, or benign, Random Region Segmentation (RSS) for image segmentation and SIFT and GLCM algorithms are applied for feature extraction technique. The model is trained considering the dataset IQ -OTH or NCCD with 300 epochs, with an accuracy of 96.5% achieved under 200 cluster formations.

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“…Rani and Jawhar [ 91 ] proposed a method of enhanced deep CNN. The method uses a deep CNN to distinguish tumor images from the LIDC database and internal clinical images by measuring tumor image regions based on Advance Target Map Superpixel’s region segmentation and nanoscale imaging theory.…”

Section: Practical Applications Of Convolutional Neural Network In Tumor Diagnosismentioning

confidence: 99%

A Survey of Computer-Aided Tumor Diagnosis Based on Convolutional Neural Network

Yan

Yao

Wang

et al. 2021

Biology

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Tumors are new tissues that are harmful to human health. The malignant tumor is one of the main diseases that seriously affect human health and threaten human life. For cancer treatment, early detection of pathological features is essential to reduce cancer mortality effectively. Traditional diagnostic methods include routine laboratory tests of the patient’s secretions, and serum, immune and genetic tests. At present, the commonly used clinical imaging examinations include X-ray, CT, MRI, SPECT scan, etc. With the emergence of new problems of radiation noise reduction, medical image noise reduction technology is more and more investigated by researchers. At the same time, doctors often need to rely on clinical experience and academic background knowledge in the follow-up diagnosis of lesions. However, it is challenging to promote clinical diagnosis technology. Therefore, due to the medical needs, research on medical imaging technology and computer-aided diagnosis appears. The advantages of a convolutional neural network in tumor diagnosis are increasingly obvious. The research on computer-aided diagnosis based on medical images of tumors has become a sharper focus in the industry. Neural networks have been commonly used to research intelligent methods to assist medical image diagnosis and have made significant progress. This paper introduces the traditional methods of computer-aided diagnosis of tumors. It introduces the segmentation and classification of tumor images as well as the diagnosis methods based on CNN to help doctors determine tumors. It provides a reference for developing a CNN computer-aided system based on tumor detection research in the future.

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Superpixel with nanoscale imaging and boosted deep convolutional neural network concept for lung tumor classification

Cited by 21 publications

References 27 publications

Lung Cancer Detection Using Modified AlexNet Architecture and Support Vector Machine

Lung Cancer Detection Using Modified AlexNet Architecture and Support Vector Machine

Triple SVM Integrated with Enhanced Random Region Segmentation for Classification of Lung Tumors

A Survey of Computer-Aided Tumor Diagnosis Based on Convolutional Neural Network

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