Domain and Histopathology Adaptations–Based Classification for Malignancy Grading System

Mudeng, Vicky; Farid, Mifta Nur; Ayana, Gelan; Choe, Se-woon

doi:10.1016/j.ajpath.2023.07.007

Cited by 4 publications

(1 citation statement)

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“…Framerate, measured in frames per second, offers insights into the real-time applicability of the detection methods. The inclusion of the F1 Score, a metric combining precision and recall, ensures a balanced evaluation that considers both false positives and false negatives [27]. [28], [29].…”

Section: Methodsmentioning

confidence: 99%

YOLO vs. CNN Algorithms: A Comparative Study in Masked Face Recognition

Dewanto,

Farid,

Rafdi Syah

et al. 2024

SJI

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Purpose: This research investigates the effectiveness of YOLO (You Only Look Once) and Convolutional Neural Network (CNN) in real-time face mask recognition, addressing the challenges posed by mask-wearing in infectious disease prevention.Method: Utilizing a diverse dataset and employing YOLO's object detection and a combined Haar Cascade Algorithm with CNN, the study evaluated key performance indicators, including accuracy, framerate, and F1 Score.Results: Results indicated that CNN outperformed YOLO in accuracy (99.3% vs. 79.3%) but operated at a slightly lower framerate. YOLO excelled in recall and precision, presenting a compelling choice for specific application needs. The research underscores the importance of considering factors beyond accuracy for informed decision-making in the realm of face mask recognition.Novelty: This research evaluates the real-time performance of YOLO and CNN algorithms in masked face recognition, highlighting the crucial balance between framerate efficiency and detection accuracy.

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Section: Methodsmentioning

confidence: 99%

YOLO vs. CNN Algorithms: A Comparative Study in Masked Face Recognition

Dewanto,

Farid,

Rafdi Syah

et al. 2024

SJI

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Multistage transfer learning for medical images

Ayana,

Dese,

Abagaro

et al. 2024

Artif Intell Rev

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Deep learning is revolutionizing various domains and significantly impacting medical image analysis. Despite notable progress, numerous challenges remain, necessitating the refinement of deep learning algorithms for optimal performance in medical image analysis. This paper explores the growing demand for precise and robust medical image analysis by focusing on an advanced deep learning technique, multistage transfer learning. Over the past decade, multistage transfer learning has emerged as a pivotal strategy, particularly in overcoming challenges associated with limited medical data and model generalization. However, the absence of well-compiled literature capturing this development remains a notable gap in the field. This exhaustive investigation endeavors to address this gap by providing a foundational understanding of how multistage transfer learning approaches confront the unique challenges posed by insufficient medical image datasets. The paper offers a detailed analysis of various multistage transfer learning types, architectures, methodologies, and strategies deployed in medical image analysis. Additionally, it delves into intrinsic challenges within this framework, providing a comprehensive overview of the current state while outlining potential directions for advancing methodologies in future research. This paper underscores the transformative potential of multistage transfer learning in medical image analysis, providing valuable guidance to researchers and healthcare professionals.

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