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

Farooq, Muhammad Shoaib; Arooj, Ansif; Alroobaea, Roobaea; Baqasah, Abdullah M.; Jabarulla, Mohamed Yaseen; Singh, Dilbag; Sardar, Ruhama

doi:10.3390/s22051803

Cited by 32 publications

(12 citation statements)

References 91 publications

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“…[42] provides a robot-based engagement method for kindergarten students with autism to strengthen their narrative abilities. NAO was used to facilitate communication and gestures amongst patients who spoke Chinese [47][48][49]. According to the researchers' findings, children are better able than physicians to conduct constructive conversations with robots.…”

Section: Children With Asd May Be Diagnosed and Theirmentioning

confidence: 99%

Evaluation of Autism Spectrum Disorder Based on the Healthcare by Using Artificial Intelligence Strategies

et al. 2023

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The behaviors of children with autism spectrum disorder (ASD) are often erratic and difficult to predict. Most of the time, they are unable to communicate effectively in their own language. Instead, they communicate using hand gestures and pointing phrases. Because of this, it can be difficult for caregivers to grasp their patients’ requirements, although early detection of the condition can make this much simpler. Assistive technology and the Internet of Things (IoT) can alleviate the absence of verbal and nonverbal communication in the community. The IoT-based solutions use machine Learning (ML) and deep learning (DL) algorithms to diagnose and enhance the lives of patients. A thorough review of ASD techniques in the setting of IoT devices is presented in this research. Identifying important trends in IoT-based health care research is the primary objective of this review. There is also a technical taxonomy for organizing the current articles on ASD algorithms and methodologies based on different factors such as AI, SS network, ML, and IoT. On the basis of criteria such as accuracy and sensitivity, the statistical and operational analyses of the examined ASD techniques are presented.

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Section: Children With Asd May Be Diagnosed and Theirmentioning

confidence: 99%

Evaluation of Autism Spectrum Disorder Based on the Healthcare by Using Artificial Intelligence Strategies

et al. 2023

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“…30 different ultrasonic techniques have been reported. Multiple reports were made on 7 techniques [39][40][41][42].…”

Section: Related Workmentioning

confidence: 99%

Liver Tumors Segmentation Using 3D SegNet Deep Learning Approach

Nallasivan¹,

Ramachandran²,

Alroobaea³

et al. 2023

Computer Systems Science and Engineering

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An ultrasonic filter detects signs of malignant tumors by analysing the image's pixel quality fluctuations caused by a liver ailment. Signs of malignant growth proximity are identified in an ultrasound filter through image pixel quality variations from a liver's condition. Those changes are more common in alcoholic liver conditions than in other etiologies of cirrhosis, suggesting that the cause may be alcohol instead of liver disease. Existing Two-Dimensional (2D) ultrasound data sets contain an accuracy rate of 85.9% and a 2D Computed Tomography (CT) data set of 91.02%. The most recent work on designing a Three-Dimensional (3D) ultrasound imaging system in or close to real-time is examined. In this article, a Deep Learning (DL) model is implemented and modified to fit liver CT segmentation, and a semantic pixel classification of road scenes is recommended. The architecture is called semantic pixel-wise segmentation and comprises a hierarchical link of encoder-decoder layers. A standard data set was used to test the proposed model for liver CT scans and the tumor accuracy in the training phase. For the normal class, we obtained 100% precision for chronic cirrhosis hepatitis (73%), offset cirrhosis (59.26%), and offensive cirrhosis (91.67%) for chronic hepatitis or cirrhosis (73,0%). The aim is to develop a Computer-Aided Detection (CAD) screening tool to detect steatosis. The results proved 98.33% exactness, 94.59% sensitivity, and 92.11% case with Convolutional Neural Networks (CNN) classification. Although the classifier's performance did not differentiate so clearly at this level, it was recommended that CNN generally perform better due to the good relationship between Area under the Receiver Operating Characteristics Curve (AUC) and accuracy.

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“…Researchers can obtain data sets publicly. However, obtaining and gathering private data without official consent raises ethical concerns, especially when the study involves sensitive patient information [233]. However, research has demonstrated that human biases, such as racial and gender bias, can be reinforced by AI systems in multiple contexts [234,235,236].…”

Section: Ethical Issuesmentioning

confidence: 99%

Advances of AI in Image-Based Computer-Aided Diagnosis: A Review

Yeasmin

2023

Preprint

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Over the past two decades, computer-aided detection or diagnosis has emerged as a highly promising field of research. Its primary goal is to enhance the diagnostic and treatment procedures for radiologists and clinicians in medical image analysis. With the help of big data and advanced Artificial Intelligence (AI) technologies, i.e., machine learning and deep learning algorithms, assist in making our healthcare system more convenient, active, efficient, and personalized. The primary goal of the literature survey study is to present a thorough overview of the most important developments related to Computer-Aided Diagnosis (CAD) systems in medical imaging. This survey holds considerable importance for researchers and professionals in both medical and computer science. Several reviews regarding specific facets of CAD in medical imaging already exist. Nevertheless, the main emphasis of this paper is on covering the complete range of CAD systems’ capabilities in medical imaging. This review article introduces the background concept used for typical CAD systems in medical imaging by outlining and comparing several methods frequently employed in recent studies. The article also offers a comprehensive and well-structured survey of CAD in medicine, drawing from a meticulous selection of relevant publications. Moreover, it describes the process of handling medical images and introduces state-of-the-art AI-based CAD technologies in medical imaging, along with the future directions of CAD. This study indicates that deep learning algorithms are the most effective way to diagnose or detect diseases.

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

Cited by 32 publications

References 91 publications

Evaluation of Autism Spectrum Disorder Based on the Healthcare by Using Artificial Intelligence Strategies

Evaluation of Autism Spectrum Disorder Based on the Healthcare by Using Artificial Intelligence Strategies

Liver Tumors Segmentation Using 3D SegNet Deep Learning Approach

Advances of AI in Image-Based Computer-Aided Diagnosis: A Review

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