Computer aided diagnosis in digital pathology application: Review and perspective approach in lung cancer classification

AlZubaidi, Abbas K.; Sideseq, Fahad B.; Faeq, Ahmed; Basil, Mena

doi:10.1109/ntict.2017.7976109

Cited by 23 publications

(16 citation statements)

References 20 publications

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“…CAD also extended to pathology image analysis. 42,43 In spite of years of research and development, the number of clinically successful CAD products with US Food and Drug Agency (FDA) approval are rather limited. 44 The new emphasis in medical imaging research is radiomics 10,11 where features are extracted from images either by quantitative measurements of objects of interest in the image 12,16 or by deep learning algorithms that learn features to support classification and risk assessment.…”

Section: Quantitative Image Analysis By Machine Learningmentioning

confidence: 99%

Open access image repositories: high-quality data to enable machine learning research

et al. 2020

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Originally motivated by the need for research reproducibility and data reuse, large-scale, open access information repositories have become key resources for training and testing of advanced machine learning applications in biomedical and clinical research. To be of value, such repositories must provide large, high-quality data sets, where quality is defined as minimising variance due to data collection protocols and data misrepresentations. Curation is the key to quality. We have constructed a large public access image repository, The Cancer Imaging Archive, dedicated to the promotion of open science to advance the global effort to diagnose and treat cancer. Drawing on this experience and our experience in applying machine learning techniques to the analysis of radiology and pathology image data, we will review the requirements placed on such information repositories by state-of-the-art machine learning applications and how these requirements can be met.

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Section: Quantitative Image Analysis By Machine Learningmentioning

confidence: 99%

Open access image repositories: high-quality data to enable machine learning research

et al. 2020

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“…Different techniques for image feature extraction are successfully implemented for early detection of lethal diseases [6]. X-ray images are resourcefully used as sources of significant information to identify diseases ahead of time [7]. Therefore, the research community has well apprehended the necessity of designing an automated system based for faster detection of COVID which has resulted in number of approaches for efficient feature detection from X-Ray images.…”

Section: Literature Surveymentioning

confidence: 99%

Covid-19 Identification with Chest X-Ray Images merging Handcrafted and Automated Features for Enhanced Feature Generalization

Das¹,

Arshad

Manjhi

et al. 2020

2020 5th International Conference on Computing, Communication and Security (ICCCS)

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Outbreak of COVID-19 pandemic has imposed a major threat to the existence of human lives. High rate of infection due to the lethal virus has caused major sufferings and premature deaths to many budding possibilities. Nevertheless, quick identification of the disease can lead to faster recovery with medical help. Unfortunately, the ratio of COVID infection to methods of early medical identification of disease is quite feeble. This has motivated researchers in designing computer aided diagnosis (CAD) systems which can identify the disease from easily available chest X-Ray images of patients. However, the success rate of identification of COVID cases is not satisfactory since the proposed systems are emphasizing on identifying pneumonia rather than separating the cause of it as COVID or non COVID. In this paper, the authors have investigated the cause of such misclassification and have identified feature generalization as one of the reasons. A feature fusion based approach is proposed by the authors to encourage generalization of input features to CAD for better identification of COVID 19. The reason for proposing the fusion based feature formation is encompassing diverse X-Ray image information by extracting descriptors with assorted techniques and uniting them with fusion. The classification results with fusion based approach have reported substantial improvements compared to individual techniques. This in turn indicates better generalization of input features with feature fusion.

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“…In region growing method we group the pixels or subparts of the image into the large region, means to say we analysis splitting method we take large image and then split it into small region on the basis of homogeneity of that region [14].…”

Section: Region Growingmentioning

confidence: 99%

Lungs Cancer Detection Using Digital Image Processing Techniques: A Review

Ullah

Bari

Ahmed

et al. 2019

Mehran Univ. res. j. eng. technol.

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From last decade, lung cancer become sign of fear among the people all over the world. As a result, many countries generate funds and give invitation to many scholars to overcome on this disease. Many researchers proposed many solutions and challenges of different phases of computer aided system to detect the lung cancer in early stages and give the facts about the lung cancer. CV (Computer Vision) play vital role to prevent lung cancer. Since image processing is necessary for computer vision, further in medical image processing there are many technical steps which are necessary to improve the performance of medical diagnostic machines. Without such steps programmer is unable to achieve accuracy given by another author using specific algorithm or technique. In this paper we highlight such steps which are used by many author in pre-processing, segmentation and classification methods of lung cancer area detection. If pre-processing and segmentation process have some ambiguity than ultimately it effects on classification process. We discuss such factors briefly so that new researchers can easily understand the situation to work further in which direction.

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Computer aided diagnosis in digital pathology application: Review and perspective approach in lung cancer classification

Cited by 23 publications

References 20 publications

Open access image repositories: high-quality data to enable machine learning research

Open access image repositories: high-quality data to enable machine learning research

Covid-19 Identification with Chest X-Ray Images merging Handcrafted and Automated Features for Enhanced Feature Generalization

Lungs Cancer Detection Using Digital Image Processing Techniques: A Review

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