Adaptive Decision Support System for On-Line Multi-Class Learning and Object Detection

Hong, Guo-Jhang; Li, Donglin; Pare, S.; Saxena, Amit; Prasad, Mukesh; Lin, Cheng‐Han

doi:10.3390/app112311268

Cited by 3 publications

(2 citation statements)

References 33 publications

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“…In recent years, there has been a growing interest in the automatic classification of skin diseases, driven by the potential to enhance diagnostic accuracy and reduce mortality rates. This is due to the surge in growth and interest in computer vision-based approaches that are low-cost and transparent in decision-making [4][5][6][7][8]. These computer-aided diagnostics (CAD) methods, which are non-invasive, rely on accurately diagnosing dermoscopic images.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: Utilizing Generative Adversarial Networks for Acne Dataset Generation in Dermatology

Sankar,

Chaturvedi,

Nayan

et al. 2024

BioMedInformatics

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Background: In recent years, computer-aided diagnosis for skin conditions has made significant strides, primarily driven by artificial intelligence (AI) solutions. However, despite this progress, the efficiency of AI-enabled systems remains hindered by the scarcity of high-quality and large-scale datasets, primarily due to privacy concerns. Methods: This research circumvents privacy issues associated with real-world acne datasets by creating a synthetic dataset of human faces with varying acne severity levels (mild, moderate, and severe) using Generative Adversarial Networks (GANs). Further, three object detection models—YOLOv5, YOLOv8, and Detectron2—are used to evaluate the efficacy of the augmented dataset for detecting acne. Results: Integrating StyleGAN with these models, the results demonstrate the mean average precision (mAP) scores: YOLOv5: 73.5%, YOLOv8: 73.6%, and Detectron2: 37.7%. These scores surpass the mAP achieved without GANs. Conclusions: This study underscores the effectiveness of GANs in generating synthetic facial acne images and emphasizes the importance of utilizing GANs and convolutional neural network (CNN) models for accurate acne detection.

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Section: Introductionmentioning

confidence: 99%

RETRACTED: Utilizing Generative Adversarial Networks for Acne Dataset Generation in Dermatology

Sankar,

Chaturvedi,

Nayan

et al. 2024

BioMedInformatics

Self Cite

View full text Add to dashboard Cite

show abstract

“…The regression sub-network outputs coordinate values of shape 4 via a fully connected layer. In object detection tasks [50][51][52], an imbalance between positive and negative samples is a major cause of classification difficulties. The Focal Loss method solves this problem by adjusting the weights of the difficult and easy-to-classify samples.…”

Section: Object Detectionmentioning

confidence: 99%

Detecting Human Falls in Poor Lighting: Object Detection and Tracking Approach for Indoor Safety

Chaturvedi

Braytee

et al. 2023

Electronics

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Falls are one the leading causes of accidental death for all people, but the elderly are at particularly high risk. Falls are severe issue in the care of those elderly people who live alone and have limited access to health aides and skilled nursing care. Conventional vision-based systems for fall detection are prone to failure in conditions with low illumination. Therefore, an automated system that detects falls in low-light conditions has become an urgent need for protecting vulnerable people. This paper proposes a novel vision-based fall detection system that uses object tracking and image enhancement techniques. The proposed approach is divided into two parts. First, the captured frames are optimized using a dual illumination estimation algorithm. Next, a deep-learning-based tracking framework that includes detection by YOLOv7 and tracking by the Deep SORT algorithm is proposed to perform fall detection. On the Le2i fall and UR fall detection (URFD) datasets, we evaluate the proposed method and demonstrate the effectiveness of fall detection in dark night environments with obstacles.

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A lightweight algorithm for pedestrian detection in overhead images

Liao,

Jin,

Yao

2024

Multimedia Systems

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Adaptive Decision Support System for On-Line Multi-Class Learning and Object Detection

Cited by 3 publications

References 33 publications

RETRACTED: Utilizing Generative Adversarial Networks for Acne Dataset Generation in Dermatology

RETRACTED: Utilizing Generative Adversarial Networks for Acne Dataset Generation in Dermatology

Detecting Human Falls in Poor Lighting: Object Detection and Tracking Approach for Indoor Safety

A lightweight algorithm for pedestrian detection in overhead images

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