Lung Cancer Detection using Curvelet Transform and Neural Network

Gupta, Bhawna; Tiwari, Shamik

doi:10.5120/14949-3082

Cited by 24 publications

(5 citation statements)

References 7 publications

(6 reference statements)

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“…Detection using artificial neural network and fuzzy clustering methods presents two segmentation methods and [4][5]7] and Early Detection and Prediction of Lung Cancer Survival using Neural Network Classifier have been developed but provide poor detection and identification [6]. Moreover, it uses Curvelet Transform and Neural Network [8], to propose a new technique for LCD identification where curvelet transforms can extract the features of lung cancer CT scan images proficiently.…”

Section: Imaging Techniques and Processingmentioning

confidence: 99%

MRI and CT for scanning human bodies

Yang

2023

TNS

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Prostate-Specific antigen (PSA) is one of the most popular biomarkers for prostate carcinoma (PCA) at present. PSA indicates initial cancer and recurrence following therapy, but does not distinguish clearly from local or remote lesions. Molecular and functional scans, which can give a thorough and complete picture of PCR amplification, are a more robust instrument for detecting and assessing the progression of cancer at the stage as well as at subsequent stages. In this paper, PET or CT, MRI are useful for diagnosing, staging and regrading of PCA, as well as their application to prognostic, therapeutic and therapeutic evaluation. Novel imaging techniques, including novel radiotracers and PET/MRI, are also presented in this paper. Looking forward, the evolution of imaging techniques holds promise for improved PCA management. Continued innovation in radiotracers and hybrid imaging technologies such as PET/MRI could revolutionize early detection, accurate staging, and effective treatment, paving the way for precision oncology in prostate cancer.

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Section: Imaging Techniques and Processingmentioning

confidence: 99%

MRI and CT for scanning human bodies

Yang

2023

TNS

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“…The work published by Kuruvilla and Gunavathi (2014) proposed a methodology based on texture features using Artificial Neural Network (ANN), with an accuracy rate of 93.30%. In their part, Gupta and Tiwari (2014) proposed a methodology based on shape features using ANN, with an accuracy rate of 90.00%. Dandil et al (2014) proposed a methodology based on texture features using Principal Component Analysis (PCA) and ANN, with an accuracy rate of 90.63%.…”

Section: Related Workmentioning

confidence: 99%

Classification of malignancy of lung nodules in CT images using Convolutional Neural Network

Silva¹,

Silva²,

Paiva³

et al. 2016

Anais Do XVI Simpósio Brasileiro De Computação Aplicada À Saúde (SBCAS 2016)

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Lung cancer presents the highest mortality rate, besides being one of the smallest survival rates after diagnosis. Thereby, early detection is extremely important for the diagnosis and treatment. This paper proposes three different architectures of Convolutional Neural Network (CNN), which is a deep learning technique, for classification of malignancy of lung nodules without computing the morphology and texture features. The methodology was tested onto the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), with the best accuracy of 82.3%, sensitivity of 79.4% and specificity 83.8%.

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“…In the world, merely one from five deaths is due to smoking and utilizing tobacco [4]. Indeed, there are two major types of lung-cancer [5], [6]: the first one is named non-small-cell cancer of lung (NSCCL) this type further categorized into squamouscell carcinoma which was constituted approximately 25% to 30% of all lung-cancer, and adenocarcinoma which comprised of nearly 40% of lung cancer. This is counted as the most common lung-cancer type that has been seen in people who are not smoking especially women and youth people like teenagers and children, and large-cell carcinoma which constituted about 10-15% of non-small-cell cancer of lung.…”

Section: Introductionmentioning

confidence: 99%

Data mining techniques for lung and breast cancer diagnosis: A review

Ahmed

2021

IJ-ICT

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<p>Today, cancer counted as the riskier disease than the other diseases in the globe. There are many cancer forms like leukemia, skin cancer, and stomach cancer but lung and breast cancer are the most common forms that many people suffered from. Cancer is the disease that cell has grown rapidly and abnormally that is why treating it is somehow tough in some cases but it can be controlled if it is detected in the initial stage. Data-mining classification algorithms had a vital role in predicting and recognizing both benign and malignant cell. Several classifiers are available to classify the usual and unusual cells such as decision-tree, artificial-neural net, SVM, and KNN. This paper presents a systematic review about the most well-known data-mining classification algorithms for lung and breast cancer diagnose. A brief review about KDD and the data-mining concept has demonstrated. The Decision-Tree (D-Tree), ANN, Support-vector-machine, and naïve Bayes classifier that is widely utilized in the biomedical field has been reviewed along with the some algorithms such as C4.5, Cart, and Iterative -Dichotomiser 3 ‘ID3’. A comparison has been done among various reviewed papers in terms of accuracy that used various data-mining classification algorithms to propose the lung and breast cancer diagnosis system. The experimental results of the reviewed papers showed that the Multilayer Perceptron (MLP) and Logistic Regression (LR) gave a higher accuracy of 99.04% and 98.1%, respectively.</p>

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Lung Cancer Detection using Curvelet Transform and Neural Network

Cited by 24 publications

References 7 publications

MRI and CT for scanning human bodies

MRI and CT for scanning human bodies

Classification of malignancy of lung nodules in CT images using Convolutional Neural Network

Data mining techniques for lung and breast cancer diagnosis: A review

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