A Survey of Computer Aided Diagnosis (Cad) of Liver in Medical Diagnosis

Farooq, Shoaib; Khan, Zoya

doi:10.21015/vtcs.v15i3.524

Cited by 6 publications

(3 citation statements)

References 13 publications

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“…(iii) Computational Cost: Deep neural networks are computationally rigorous, multilayered algorithms consisting of millions of parameters. Therefore, the convergence of such algorithms requires more computational power as well as running time [29,38,95]. Although there is no strict rule about how much data are required to optimally train the neural network, experiential studies recommend that tenfold training data produce an effective model.…”

Section: Dr Detection Challengesmentioning

confidence: 99%

Untangling Computer-Aided Diagnostic System for Screening Diabetic Retinopathy Based on Deep Learning Techniques

Farooq

Arooj

Alroobaea

et al. 2022

Sensors

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Diabetic Retinopathy (DR) is a predominant cause of visual impairment and loss. Approximately 285 million worldwide population is affected with diabetes, and one-third of these patients have symptoms of DR. Specifically, it tends to affect the patients with 20 years or more with diabetes, but it can be reduced by early detection and proper treatment. Diagnosis of DR by using manual methods is a time-consuming and expensive task which involves trained ophthalmologists to observe and evaluate DR using digital fundus images of the retina. This study aims to systematically find and analyze high-quality research work for the diagnosis of DR using deep learning approaches. This research comprehends the DR grading, staging protocols and also presents the DR taxonomy. Furthermore, identifies, compares, and investigates the deep learning-based algorithms, techniques, and, methods for classifying DR stages. Various publicly available dataset used for deep learning have also been analyzed and dispensed for descriptive and empirical understanding for real-time DR applications. Our in-depth study shows that in the last few years there has been an increasing inclination towards deep learning approaches. 35% of the studies have used Convolutional Neural Networks (CNNs), 26% implemented the Ensemble CNN (ECNN) and, 13% Deep Neural Networks (DNN) are amongst the most used algorithms for the DR classification. Thus using the deep learning algorithms for DR diagnostics have future research potential for DR early detection and prevention based solution.

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Section: Dr Detection Challengesmentioning

confidence: 99%

Untangling Computer-Aided Diagnostic System for Screening Diabetic Retinopathy Based on Deep Learning Techniques

Farooq

Arooj

Alroobaea

et al. 2022

Sensors

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show abstract

“…They purpose them to effectively provide the aggregated information concerning the patients through the doctors, procedures, diseases and clinical analysis, including developing novel care products, process enhancement and result management. Moreover, the healthcare practitioners' purpose is to save money that would have been spent in the paper works, filling the expenses, the time that would have been spent looking for regulatory reporting and physical records [8].…”

Section: Background Analysis Of the Medical Record Systemsmentioning

confidence: 99%

Formulating the Business Case for Hospital Information Systems – Analysis of Kaiser Permanente Investment Choice

McLean

2021

International Journal of Advanced Information and Communication Technology

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This research paper focusses on the business case for the Hospital Information Systems (HIS) which represents the businesses that contemplate the fundamental investment in the Electronic Medical Record (EMR) information technology framework. In that case, Kaiser permanent framework is tasked with the obligation to maintain social mission which will develop the medical fields and can be applied by medical practitioners to effectively develop the clinical sector. Nonetheless, Kaiser framework might be stimulated to underscore just like many investment organizational cases, the data presented in this framework signifies our ‘effective thinking’ at a certain timeframe in relation to limited information. This represents both the internalized Kaiser Permanent information and the paucity of the essential sets of data in the wide-range medical and bioinformatics sector. We project that this research contributes to the upcoming measure of EMR to the clinical aspect of communities and patients. Moreover, there are critical analyses that have been executed in relation to the medical information technology and has been designed based on the framework’s cost and benefit analysis for the electronic HIS.

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“…Biology has incredibly been transformed from the significantly descriptive sciences channelled to a number of principles and theories such as the evolution theory by Charles Darwin, protocols of Mendel, centralized molecular dogmas of biology and the structural DNA in the human cells and the ecological or organismal contexts. BI has significantly emerged as a computational framework that drives the insight behind the evaluation of big data supporting the identification of genomics, metabolomics and proteomics [9]. As such, BI is considered predominantly connected to a number of research practices.…”

Section: Scientific Contents and Informatics Tasks Of Bi And MImentioning

confidence: 99%

An Analysis of Medical Informatics and Application of Computer-Aided Decision Support Framework

Chen

2020

JMIC

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Due to the advent of technology in the medical sector, the future of the Computer-Aided Decision (CAD) is promising as a support system for the processing of images. Since there is significant results reported due to the application of diagnostic radiology in the healthcare facility, radiologists are looking forward to enhancing medical and bioinformatics using CAD. Medical evaluations and trials have been done over the past few decades to aid in the optimization of accurate programs and evaluate the real contributions of CAD in the medical informatics interpretation procedures. Health experts and radiologists utilizing patient outputs from fundamental application of CAD are placed in the best position to focus on the final decisions concerning the performance of patients and diagnosis. However, researches have shown that the computer outputs require not projecting significant general accuracy compared to a certain radiologist to enhance patients’ performance. The volume and measure of the present patient data including their complexity to enhance the process of making proper healthcare decisions while making it problematic for healthcare practitioners and physicians to facilitate the management of patients. This condition calls for the usage of biomedical informatics methodologies to effectively process information, create biomedical implementations and informatics frameworks for CAD support systems. With that regard, this paper evaluates the medical and bioinformatics based on the application of CAD systems. It further projects on the applications of the systems, their application guidelines and techniques. The paper ends with the analysis of the future problems and directions of the CAD support framework.

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A Survey of Computer Aided Diagnosis (Cad) of Liver in Medical Diagnosis

Cited by 6 publications

References 13 publications

Untangling Computer-Aided Diagnostic System for Screening Diabetic Retinopathy Based on Deep Learning Techniques

Untangling Computer-Aided Diagnostic System for Screening Diabetic Retinopathy Based on Deep Learning Techniques

Formulating the Business Case for Hospital Information Systems – Analysis of Kaiser Permanente Investment Choice

An Analysis of Medical Informatics and Application of Computer-Aided Decision Support Framework

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