Lung cancer detection using Local Energy-based Shape Histogram (LESH) feature extraction and cognitive machine learning techniques

Wajid, Summrina Kanwal; Hussain, Amir; Huang, Kaizhu; Boulila, ‪Wadii

doi:10.1109/icci-cc.2016.7862060

Cited by 14 publications

(6 citation statements)

References 26 publications

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“…In CLAHE, the diferentiation enhancement near given pixel value is provided by the incline of the change work [186]. Punithavathy et al [187], Bhagyarekha and Pise [188], and Wajid et al [189] used CLAHE as image preprocessing methodology. Technically, CLAHE does this by setting a threshold.…”

Section: Contrast Limited Adaptive Histogram Equalization (Clahe) Con...mentioning

confidence: 99%

A Comprehensive Survey on the Progress, Process, and Challenges of Lung Cancer Detection and Classification

Mridha

Prodeep

Hoque

et al. 2022

Journal of Healthcare Engineering

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Lung cancer is the primary reason of cancer deaths worldwide, and the percentage of death rate is increasing step by step. There are chances of recovering from lung cancer by detecting it early. In any case, because the number of radiologists is limited and they have been working overtime, the increase in image data makes it hard for them to evaluate the images accurately. As a result, many researchers have come up with automated ways to predict the growth of cancer cells using medical imaging methods in a quick and accurate way. Previously, a lot of work was done on computer-aided detection (CADe) and computer-aided diagnosis (CADx) in computed tomography (CT) scan, magnetic resonance imaging (MRI), and X-ray with the goal of effective detection and segmentation of pulmonary nodule, as well as classifying nodules as malignant or benign. But still, no complete comprehensive review that includes all aspects of lung cancer has been done. In this paper, every aspect of lung cancer is discussed in detail, including datasets, image preprocessing, segmentation methods, optimal feature extraction and selection methods, evaluation measurement matrices, and classifiers. Finally, the study looks into several lung cancer-related issues with possible solutions.

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Section: Contrast Limited Adaptive Histogram Equalization (Clahe) Con...mentioning

confidence: 99%

A Comprehensive Survey on the Progress, Process, and Challenges of Lung Cancer Detection and Classification

Mridha

Prodeep

Hoque

et al. 2022

Journal of Healthcare Engineering

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“…Our study scope covers both the generalization behaviour of trained deep learning models and the effect that commonly used pre-processing techniques have on that generalization behaviour. To meet this objective our models have been trained and tested not only against raw data, but also using simple histogram equalization using parameters matching ImageNet (Krizhevsky et al, 2017) and CLAHE as successfully employed in a number of studies into deep learning based lung pathology detection from medical images (Sarkar et al, 2020;Wajid et al, 2017). We were also interested in the effect of handcrafted feature extraction layers, often implemented as wavelet filters such as Gabor filters, which have been shown to improve the accuracy of deep learning classification networks (Han et al, 2014;Paulraj & Chellliah, 2018;Ye et al, 2007) in thoracic disease imaging applications.…”

Section: Pre-processing Pipelinesmentioning

confidence: 99%

Systematic investigation into generalization of COVID-19 CT deep learning models with Gabor ensemble for lung involvement scoring

Horry¹,

Chakraborty²,

Pradhan³

et al. 2021

Preprint

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The COVID-19 pandemic has inspired unprecedented data collection and computer vision modelling efforts worldwide, focusing on diagnosis and stratification of COVID-19 from medical images. Despite this large-scale research effort, these models have found limited practical application due in part to unproven generalization of these models beyond their source study. This study investigates the generalizability of key published models using the publicly available COVID-19 Computed Tomography data through cross dataset validation. We then assess the predictive ability of these models for COVID-19 severity using an independent new dataset that is stratified for COVID-19 lung involvement. Each inter-dataset study is performed using histogram equalization, and contrast limited adaptive histogram equalization with and without a learning Gabor filter. The study shows high variability in the generalization of models trained on these datasets due to varied sample image provenances and acquisition processes amongst other factors. We show that under certain conditions, an internally consistent dataset can generalize well to an external dataset despite structural differences between these datasets with f1 scores up to 86%. Our best performing model shows high predictive accuracy for lung involvement score for an independent dataset for which expertly labelled lung involvement stratification is available. Creating an ensemble of our best model for disease positive prediction with our best model for disease negative prediction using a min-max function resulted in a superior model for lung involvement prediction with average predictive accuracy of 75% for zero lung involvement and 96% for 75-100% lung involvement with almost linear relationship between these stratifications.

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“…Its response highlights edges and borders, making it useful to detect shadows, retinal layers, cyst membranes, and other high contrast non-homogeneous elements that may be present in the region of interest. This feature descriptor has proven its usefulness in other medical imaging domains, being successfully used to detect breast [38] and lung [39] cancer. These cases also present tissues with an heterogeneity difference between healthy and malignant regions.…”

Section: Local Energy-based Shape Histogram (Lesh)mentioning

confidence: 99%

Intraretinal Fluid Pattern Characterization in Optical Coherence Tomography Images

Moura

Vidal

Novo

et al. 2020

Sensors

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Optical Coherence Tomography (OCT) has become a relevant image modality in the ophthalmological clinical practice, as it offers a detailed representation of the eye fundus. This medical imaging modality is currently one of the main means of identification and characterization of intraretinal cystoid regions, a crucial task in the diagnosis of exudative macular disease or macular edema, among the main causes of blindness in developed countries. This work presents an exhaustive analysis of intensity and texture-based descriptors for its identification and classification, using a complete set of 510 texture features, three state-of-the-art feature selection strategies, and seven representative classifier strategies. The methodology validation and the analysis were performed using an image dataset of 83 OCT scans. From these images, 1609 samples were extracted from both cystoid and non-cystoid regions. The different tested configurations provided satisfactory results, reaching a mean cross-validation test accuracy of 92.69%. The most promising feature categories identified for the issue were the Gabor filters, the Histogram of Oriented Gradients (HOG), the Gray-Level Run-Length matrix (GLRL), and the Laws’ texture filters (LAWS), being consistently and considerably selected along all feature selector algorithms in the top positions of different relevance rankings.

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Lung cancer detection using Local Energy-based Shape Histogram (LESH) feature extraction and cognitive machine learning techniques

Cited by 14 publications

References 26 publications

A Comprehensive Survey on the Progress, Process, and Challenges of Lung Cancer Detection and Classification

A Comprehensive Survey on the Progress, Process, and Challenges of Lung Cancer Detection and Classification

Systematic investigation into generalization of COVID-19 CT deep learning models with Gabor ensemble for lung involvement scoring

Intraretinal Fluid Pattern Characterization in Optical Coherence Tomography Images

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