Automated Diagnosis of Breast Cancer with RoI Detection Using YOLO and Heuristics

Bal, Ananya; Das, Meenakshi; Satapathy, Shashank Mouli; Jena, Madhusmita; Das, Subha Kanta

doi:10.1007/978-3-030-65621-8_16

Distributed Computing and Internet Technology

2020

DOI: 10.1007/978-3-030-65621-8_16

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Automated Diagnosis of Breast Cancer with RoI Detection Using YOLO and Heuristics

Ananya Bal

Meenakshi Das

Shashank Mouli Satapathy

et al.

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Cited by 2 publications

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An artificial intelligence-enabled consumables tracking system for medical laboratories

Sritart,

Tosranon,

Taertulakarn

2024

Journal of Intelligent Systems

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The medical laboratory plays a crucial role within a hospital setting and is responsible for the examination and analysis of patient specimens to accurately diagnose various ailments. The burden on medical laboratory personnel has significantly increased, particularly in the context of the ongoing global COVID-19 pandemic. Worldwide, the implementation of comprehensive and extended COVID-19 screening programs has placed a significant strain on healthcare professionals. This burden has led to exhaustion among medical employees, limiting their ability to effectively track laboratory resources, such as medical equipment and consumables. Therefore, this study proposed an artificial intelligence (AI)-based solution that contributes to a more efficient and less labor-intensive workflow for medical workers in laboratory settings. With the ultimate goal to reduce the burden on healthcare providers by streamlining the process of monitoring and managing these resources, the objective of this study is to design and develop an AI-based system for consumables tracking in medical laboratories. In this work, the effectiveness of two object detection models, namely, YOLOv5x6 and YOLOv8l, for the administration of consumables in medical laboratories was evaluated and analyzed. A total of 570 photographs were used to create the dataset, capturing the objects in a variety of settings. The findings indicate that both detection models demonstrate a notable capability to achieve a high mean average precision. This underscores the effectiveness of computer vision in the context of consumable goods detection scenarios and provides a reference for the application of real-time detection models in tracking systems within medical laboratories.

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An artificial intelligence-enabled consumables tracking system for medical laboratories

Sritart,

Tosranon,

Taertulakarn

2024

Journal of Intelligent Systems

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A Technical Comparison of YOLO-Based Chest Cancer Diagnosis Methods

Shi¹

2023

HSET

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Cancers have become one of the deadliest diseases in the world, and early diagnosis becomes vital for a patient's survival. As deep learning advances, YOLO has become an attractive tool as it supports real-time interactions. Thus, YOLO is expected to be applied in cancer diagnosis. A technical study of a YOLO-based computer aid diagnosis system for chest cancers is presented in the paper. Four kinds of the image in cancer diagnosis, histopathological images, mammograms, CTs, and Low-dose CTs, are introduced. Three issues of implementing a computer aid diagnosis system (CAD) are discussed and analyzed, including the usage of handcrafted features, the high false positive rate in clinical practice, and difficulty in detecting irregular nodules in spiral CTs. In discussion, the drawback of handcrafted features in the region of interest (ROI) extraction can be addressed by applying extra architectures like ResNet50 as extractors. A trained network can serve as a non-nodule filter to reduce the false positive rate in diagnosis. Image data can be categorized based on morphological features in data preprocessing to train a more sensitive model, then irregular-shape nodules can be detected by CAD.

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Automated Diagnosis of Breast Cancer with RoI Detection Using YOLO and Heuristics

Cited by 2 publications

References 12 publications

An artificial intelligence-enabled consumables tracking system for medical laboratories

An artificial intelligence-enabled consumables tracking system for medical laboratories

A Technical Comparison of YOLO-Based Chest Cancer Diagnosis Methods

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