Enhancement of fissure using back propagation neural network and segmentation of lobes in CT scan image

Thanammal, K. K.; Sudha, J. S. Jaya

doi:10.1504/ijbet.2016.074108

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…This research showed that the application of segmentation could apply accuracy to the neural network method. The accuracy obtained in this research was 95% [17]. The application of neural network for lung cancer CT scan data has been conducted by Arulmurugan et al [18] and Shaukat et al [19].…”

Section: Introductionmentioning

confidence: 50%

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Lung cancer classification based on CT scan image by applying FCM segmentation and neural network technique

2021

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The number of people with lung cancer has reached approximately 2.09 million people worldwide. Out of 9.06 million cases of death, 1.76 million people die due to lung cancer. Lung cancer can be automatically identified using a computer-aided diagnosis system (CAD) such as image processing. The steps taken for early detection are pre-processing feature extraction, and classification. Pre-processing is carried out in several stages, namely grayscale images, noise removal, and contrast limited adaptive histogram equalization. This image feature extracted using gray level co-occurrence matrix (GLCM) and classified using 2 method of neural network which is feed forward neural network (FFNN) dan feed backward neural network (FBNN). This research aims to obtain the best neural network model to classify lung cancer a. Based on training time and accuracy, the best method of FFNN is kernel extreme learning machine (KELM), with a training time of 12 seconds and an accuracy of 93.45%, while the best method of FBNN is Backpropagation with a training time of 18 minutes 04 seconds and an accuracy of 97.5%.

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Section: Introductionmentioning

confidence: 50%

“…In this research, several neural network methods are used to form the best model of a system. Neural network has been applied to CT scan data by Thanammal and Sudha [17]. This research used one of the neural network methods that is Backpropagation.…”

Section: Introductionmentioning

confidence: 99%

Lung cancer classification based on CT scan image by applying FCM segmentation and neural network technique

2021

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“…For the binary disease classification/survival prediction tasks, we used a conservative approach, thresholding the voxels at -570 HU and using morphological closing and opening operations to create smooth lung masks that contain lung parenchyma and air. To further include pathological signs such as fungus balls and pleura-thickening, we refined the approach from [4] and [5]. First, we define a set of markers.…”

Section: Lung Segmentationmentioning

confidence: 99%

SAPSAM - Sparsely Annotated Pathological Sign Activation Maps - A Novel Approach To Train Convolutional Neural Networks On Lung CT Scans Using Binary Labels Only

Zusag

Paolo

Semple

et al. 2019

2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)

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Chronic Pulmonary Aspergillosis (CPA) is a complex lung disease caused by infection with Aspergillus. Computed tomography (CT) images are frequently requested in patients with suspected and established disease, but the radiological signs on CT are difficult to quantify making accurate followup challenging. We propose a novel method to train Convolutional Neural Networks using only regional labels on the presence of pathological signs, to not only detect CPA, but also spatially localize pathological signs. We use average intensity projections within different ranges of Hounsfield-unit (HU) values, transforming input 3D CT scans into 2D RGBlike images. CNN architectures are trained for hierarchical tasks, leading to precise activation maps of pathological patterns. Results on a cohort of 352 subjects demonstrate high classification accuracy, localization precision and predictive power of 2 year survival. Such tool opens the way to CPA patient stratification and quantitative follow-up of CPA pathological signs, for patients under drug therapy.

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Sooty-LuCaNet: Sooty tern optimization based deep learning network for lung cancer detection

Muthazhagan¹,

Ravi²,

Rajinigirinath

2023

IFS

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Lung cancer is the prevalent malignancy afflicting both men and women, mostly affects the chain smokers. The lung CT images are examined to identifying the abnormalities, but diagnosing lung cancer with CT images is time-consuming and difficult task. In this work, a novel Sooty-LuCaNet has been proposed in which the best features are selected using sooty tern optimization to reduces computational complexity of neural network. Initially, the denoised CT images are segmented using Grabcut technique to separate the lung nodules by eliminating the background distortions. The deep learning based Shufflenet is used to extract the structural features from the segmented nodule and the textural features from the enhanced images. Afterwards, the sooty tern optimization (STO) algorithm is applied to select the most relevant features from the extracted features from the ShuffleNet. Finally, the classification process is carried out to differentiate the normal, small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) from the CT images. The experimental findings show the robustness of the proposed Sooty-LuCaNet based on the specific metrics namely sensitivity, accuracy, specificity, recall, precision and F1 score. An average classification accuracy of 99.16% is achieved for detection and classification of lung cancer.

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Enhancement of fissure using back propagation neural network and segmentation of lobes in CT scan image

Cited by 3 publications

References 9 publications

Lung cancer classification based on CT scan image by applying FCM segmentation and neural network technique

Lung cancer classification based on CT scan image by applying FCM segmentation and neural network technique

SAPSAM - Sparsely Annotated Pathological Sign Activation Maps - A Novel Approach To Train Convolutional Neural Networks On Lung CT Scans Using Binary Labels Only

Sooty-LuCaNet: Sooty tern optimization based deep learning network for lung cancer detection

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