Novel computer‐aided diagnosis of lung cancer using bag of visual words to achieve high accuracy rates

Chellan, Thinkal Dayana; Chellappan, Agees Kumar

doi:10.1049/joe.2018.5215

Cited by 5 publications

(2 citation statements)

References 11 publications

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“…It is obvious from this table that the performance of different deep‐learning models is better than any data set with hand‐engineered feature extraction method for lung cancer. The proposed BDCNN is contrasted with current methods, such as bag of visual word (BoVW) classifier, multiclass SVM (MVSM) classifier, feed forward neural network (FFNN), artificial neural network (ANN), bagged random tree (BRT) classifier, and deep neural network (DNN) 25,31 …”

Section: Resultsmentioning

confidence: 99%

“…Usually, the convolutional neural network is used in this deep learning process; here binary image features are automatically extracted based on the process of classification of features being carried out. The method evaluation period was greater 30‐33 used a superpixel and density‐based spatial clustering approach for segmentation of the lung nodule picture sequences. From the overall survey, the goal of the proposed method is to increase accuracy and reduce analytical time, false‐positive reduction, and subjectivity to segmentation with CT‐scan studies.…”

Section: Introductionmentioning

confidence: 99%

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

Rani

Jawhar

2020

Int J Imaging Syst Tech

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Lung tumor is a complex illness caused by irregular lung cell growth. Earlier tumor detection is a key factor in effective treatment planning. When assessing the lung computed tomography, the doctor has many difficulties when determining the precise tumor boundaries. By offering the radiologist a second opinion and helping to improve the sensitivity and accuracy of tumor detection, the use of computer‐aided diagnosis could be near as effective. In this research article, the proposed Lung Tumor Detection Algorithm consists of four phases: image acquisition, preprocessing, segmentation, and classification. The Advance Target Map Superpixel‐based Region Segmentation Algorithm is proposed for segmentation purposes, and then the tumor region is measured using the nanoimaging theory. Using the concept of boosted deep convolutional neural network yields 97.3% precision, image recognition can be achieved. In the types of literature with the current method, which shows the study's proposed efficacy, the implementation of the proposed approach is found dramatically.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%