Pulmonary Emphysema Analysis Using Shearlet Based Textures and Radial Basis Function Network

Semunigus, Wogderes

doi:10.29284/ijasis.6.1.2020.1-11

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…Both VGG and ResNet, which are both CNN architectures, are trained, and then their performances are compared [15]. Different architectures are discussed using supervised ResNet and SeNet [16], Shearlet [17] and image fusion [18].…”

Section: Related Workmentioning

confidence: 99%

Pattern Knowledge Discovery Based Lung Cancer Classification System

Mangayarkarasi¹,

Chithrakkannan²,

Karthikeyan³

2023

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An accurate detection of abnormal lung nodule detection is very important for effective treatment and surgical procedure to remove the nodules. This paper introduces an efficient deep learning model to classify lung cancer in both left and right lung. It consists of three important stages; preprocessing, lung region detection and abnormal lung nodule detection. Further, a detailed discussion about the performance of the system is given using two benchmark databases; 30 lung CT images taken from the ELCAP dataset and 130 lung CT images taken from the LIDC dataset. An algorithmic framework is first created for the purpose of segmenting left and right lung region by a morphological algorithm after removing the noise by a wiener filter. A well defined deep learning architecture is designed for effective classification or detection of abnormal lung nodule detection by semantic classification. The proposed system is validated on LIDC and ELCAP database and provides an average accuracy of 97.86%.

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Section: Related Workmentioning

confidence: 99%

Pattern Knowledge Discovery Based Lung Cancer Classification System

Mangayarkarasi¹,

Chithrakkannan²,

Karthikeyan³

2023

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“…Table 1 shows the extracted texture features. Before feature extraction, each component of HSV image is represented by SOS [22,23]. From the obtained SOS subbands, the stochastic texture features are extracted.…”

Section: Feature Extractionmentioning

confidence: 99%

A Shearlet-Based Second Order System for Classifying Oral Cancer: An Analysis of Histopathological Images

Gopi,

Visumathi,

Jayanthi

et al. 2023

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In this study, we propose a Second Order Shearlets (SOS) based system for Oral Cancer Classification (OCC) that leverages histopathological images. The fundamental premise of the system is the observable variations in texture patterns between normal and abnormal cells within these images, which can be exploited for differentiation. The images undergo a transformation from the Red-Green-Blue (RGB) color space to the Hue-Saturation-Value (HSV) color space, followed by the extraction of co-occurrence texture features via the SOS system. Further enhancement of feature extraction is achieved by applying a median filter for de-noising the histopathological images. The proposed SOS-OCC system, equipped with a probabilistic classifier at the final stage, was presented with an assortment of 1224 images for evaluation. The results indicated a noteworthy classification accuracy of 98.6% when employing stratified k-fold cross-validation, thereby underlining the system's efficacy in identifying oral cancer-related abnormalities. Moreover, a comparative analysis was conducted with Wavelet, Curvelet, and Contourlet-based representation systems to underscore the superior performance of the SOS-OCC system. This study provides valuable insights into the application of the SOS approach to oral cancer classification and sets a promising precedent for future research.

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“…The risk of developing cancer in India is approximately 9.8% in males and 9.4% in females before the age of 75 years whereas the risk of dying from various cancers is 7.3% in males and 6.3% in females. In India, Lung cancer is second leading cancer in males and the first one is lip and oral cavity in males and breast cancer in females [2][3]. Figure 3 shows the cancer incidence and mortality in 2018.…”

Section: Lung Cancer Statisticsmentioning

confidence: 99%

CT Lung Image Classification Systems - A Systematic Review

Prabhakar¹,

Sivasubramanian²,

Sundaram³

et al. 2023

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Computer Aided Detection (CAD) and diagnostic technologies and their application have emerged in the last decade. CAD applications have concentrated on identification of the region of interest, for example nodules in the lung in the images, and analysis of the characteristics of objects. CAD technologies take advantages of computer systems as well as image processing and analysis techniques to overcome perceptual and interpretive errors caused by radiologists in the process of image observation and interpretation. This fact is inevitable in the context of medical practice since experience of image readers may vary and each observer (such as a radiologist) has a unique visual search on an image. Furthermore, there are always inter and intra observer variability for interpretation of an image. To overcome these difficulties, different CAD systems are developed for medical image analysis. This paper presents a systematic review on different CAD systems for lung cancer classification using computed tomography images.

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Pulmonary Emphysema Analysis Using Shearlet Based Textures and Radial Basis Function Network

Cited by 3 publications

References 15 publications

Pattern Knowledge Discovery Based Lung Cancer Classification System

Pattern Knowledge Discovery Based Lung Cancer Classification System

A Shearlet-Based Second Order System for Classifying Oral Cancer: An Analysis of Histopathological Images

CT Lung Image Classification Systems - A Systematic Review

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