Performance evaluation of mask R-CNN for lung segmentation using computed tomographic images

Nam, Kibok; Lee, Dong Hoon; Kang, Sung Hak; Lee, Seungwan

doi:10.1007/s40042-022-00532-9

Cited by 2 publications

(2 citation statements)

References 15 publications

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“…ADAMW optimization is a SGD method based on adaptively estimating first-and second-order moments, and it includes a method to decay weights. The ADAMW optimizers were proposed as a solution to the overfitting and steep learning rate drop issues that plagued the ADAM optimizer [35]. By separating the weight decay from the gradient updates, ADAMW, a stochastic optimization technique, addresses the known convergence issues with ADAM by altering the standard implementation of weight decay.…”

Section: Optimization Algorithm For Deep Learningmentioning

confidence: 99%

“…In improving its performance deep learning has various algorithm optimizers such as stochastic gradient descent (SGD), ADAM, Adagard [34]. Some of these algorithms have limitations, therefore there is a new algorithm, namely the ADAMW optimizer, to overcome the ADAM optimizer problem with overfitting and a sharp decrease in learning rate [35]. Meanwhile, to improve multi-object tracking performance in deep learning, there is the Bot-sort algorithm [36] which is employed to extract movement characteristics from objects, such as displacement, acceleration, and speed; these attributes are then utilized to preserve modifications and ignore failures to identify the desired behavior in the behavior of the chickens [37].…”

Section: Introductionmentioning

confidence: 99%

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Chicken tracking for location mapping of lameness chickens using YOLOv8 and deep learning-based tracking algorithm

Triyanto,

Adi,

Suseno

2024

IJEECS

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The chicken farming industry is one of the biggest food industries that supports the achievement of food security internationally. Farmers need an independent tool that can monitor the welfare conditions of chickens in cages. Using their tools, farmers can ideally detect the condition of chickens. Lameness chickens, can be known for activity and dredging of their location in the cage. Occlusion, and background in the cage are interesting challenges. By observing behavior, image handling practices can be used to identify tainted chicks and provide an early warning of sickness in chickens. In this study, you only look once, version 8 (YOLOv8) which is a convolutional neural network (CNN) network model was chosen to perform the detection, tracking, and mapping of chicken locations. YOLOv8 was combined with various algorithm optimizers to improve training performance, such as root mean square (RMS) Prop, stochastic gradient descent (SGD), ADAM, and ADAMW. Multi-object tracking algorithms such as BOT-sort and ByteTrack are also used to improve tracking performance. Based on the results, YOLOv8 with combinations of optimizer algorithms ADAMW has the best mAP, support, precision and F1-score values compared to the others, with 0.936, 0.993, 0.990, 0.991. Meanwhile, for multi object tracking, ByteTrack is faster in inference time(s) values compared to the others, with 0.2.

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Section: Optimization Algorithm For Deep Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Chicken tracking for location mapping of lameness chickens using YOLOv8 and deep learning-based tracking algorithm

Triyanto,

Adi,

Suseno

2024

IJEECS

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Pulmonary Nodule Detection, Segmentation and Classification Using Deep Learning: A Comprehensive Literature Review

Marinakis,

Karampidis,

Papadourakis

2024

BioMedInformatics

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Lung cancer is a leading cause of cancer-related deaths worldwide, emphasizing the significance of early detection. Computer-aided diagnostic systems have emerged as valuable tools for aiding radiologists in the analysis of medical images, particularly in the context of lung cancer screening. A typical pipeline for lung cancer diagnosis involves pulmonary nodule detection, segmentation, and classification. Although traditional machine learning methods have been deployed in the previous years with great success, this literature review focuses on state-of-the-art deep learning methods. The objective is to extract key insights and methodologies from deep learning studies that exhibit high experimental results in this domain. This paper delves into the databases utilized, preprocessing steps applied, data augmentation techniques employed, and proposed methods deployed in studies with exceptional outcomes. The reviewed studies predominantly harness cutting-edge deep learning methodologies, encompassing traditional convolutional neural networks (CNNs) and advanced variants such as 3D CNNs, alongside other innovative approaches such as Capsule networks and transformers. The methods examined in these studies reflect the continuous evolution of deep learning techniques for pulmonary nodule detection, segmentation, and classification. The methodologies, datasets, and techniques discussed here collectively contribute to the development of more efficient computer-aided diagnostic systems, empowering radiologists and dfhealthcare professionals in the fight against this deadly disease.

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Performance evaluation of mask R-CNN for lung segmentation using computed tomographic images

Cited by 2 publications

References 15 publications

Chicken tracking for location mapping of lameness chickens using YOLOv8 and deep learning-based tracking algorithm

Chicken tracking for location mapping of lameness chickens using YOLOv8 and deep learning-based tracking algorithm

Pulmonary Nodule Detection, Segmentation and Classification Using Deep Learning: A Comprehensive Literature Review

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