Faster R-CNN Model With Momentum Optimizer for RBC and WBC Variants Classification

Tobias, Rogelio Ruzcko; Jesus, Luigi Carlo De; Mital, Matt Ervin; Lauguico, Sandy; Guillermo, Marielet; Vicerra, Ryan Rhay P.; Dadios, Elmer P.

doi:10.1109/lifetech48969.2020.1570619208

Cited by 18 publications

(4 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This research has provided a comprehensive exploration of Region-Based Convolutional Neural Networks (R-CNN) and its variants, shedding light on their evolution, functionalities, and impact in the domain of object detection within computer vision [25]. It emphasizes the significance of computer vision and its transformative capabilities that it brings to various industries.…”

Section: Discussionmentioning

confidence: 99%

“…Fast R-CNN and Faster R-CNN are more robust to occlusions due to their use of region proposals, but they can still be challenged by complex scenes with multiple objects. Mask R-CNN is the most robust of the variants, as it can handle occlusions and complex scenes due to its ability to predict pixel-level masks [25][26][27][28][29]. R-CNN is computationally expensive due to individual feature extraction for each region proposal.…”

Section: Performance Evaluationmentioning

confidence: 99%

See 1 more Smart Citation

Comprehensive Review of R-CNN and its Variant Architectures

Sumit,

Shrishti Bisht,

Sunita Joshi

et al. 2024

Int Res J Adv Engg Hub

View full text Add to dashboard Cite

This research paper delves into the intricacies of Region-based Convolutional Neural Networks (R- CNN) and its variants, providing a meticulous comparative analysis. The study encompasses the evolution, functionality, and impact of these models in the realm of object detection within computer vision [3]. The objectives of this paper are to elucidate the significance of R-CNN and its variants, present an overview of object detection challenges, and underscore the crucial role these models play in addressing these challenges. The methodologies employed in this research involve an in-depth examination of the architectural components, such as Selective Search, feature extraction, object classification, and bounding box regression that constitute the R-CNN approach. By reviewing the historical context and subsequent developments, including Fast R-CNN, Faster R-CNN, and Mask R-CNN, the paper aims to shed light on the continuous evolution of these models. The findings of this study aim to contribute to the broader understanding of the advancements in object detection through deep learning, with potential applications in diverse fields, including autonomous driving and face recognition.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

Comprehensive Review of R-CNN and its Variant Architectures

Sumit,

Shrishti Bisht,

Sunita Joshi

et al. 2024

Int Res J Adv Engg Hub

View full text Add to dashboard Cite

show abstract

“…The Momentum optimizer 9 adds momentum to SGD according to the accumulated historical gradient information. The gradient descent of the method is updated according to:…”

Section: Deep Reinforcement Learning In Power Controlmentioning

confidence: 99%

Power control of spectrum sharing for USVs via deep Q-networks

Zhu

Liu²,

Feng³

et al. 2022

International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022)

View full text Add to dashboard Cite

It has been recognized that power control strategy of spectrum sharing for USVs(unmanned surface vessels) counts. The policy for power control of a major USV is proposed to increase profit of spectrum resources. In the environment of an USV sailing on the sea, it is essential for the major USV to communicate effectively with the minor USV. We propose a learning-based power control approach for the major USV to share the joint spectrum with the minor USV. Specifically, the major USV updates its transmitted power abide by a power control policy that is defined beforehand. The minor USV is unaware of the major USV’s condition of transmitting power. The proposed method exploiting the strengths of the Deep Q-Network (DQN), could help the minor USV adjust its transmitting power and make full use of the empty spectrum. Both can transmit information favourably accompanied by the satisfactory service quality. The results of our experiment show that the minor USV communicate with the major USV effectively through our method. Compared to the traditional reinforcement learning method DQN, our method achieves better results.

show abstract

“…The study [20] used Faster Region-based Convolutional Neural Network to detect and clasify red blood cells (RBCs) and white blood cells (WBCs). the aims of this reseach was create fast system to helpe the medical field in the classification of RBCs and WBCs.…”

Section: Related Workmentioning

confidence: 99%

Diagnosing the Leukemia using Faster Region based Convolutional Neural Network

ABAS¹

2022

JASTT

View full text Add to dashboard Cite

It is difficult to building deep learning algorithms for identifying chronic diseases. One of the must difficulties facing the system of diagnosing leukemia is the irregular shape and twisted nucleus in white blood cells (WBCs) without cleaning and segmentation of cells by Appling filters. Moreover, it is challenge to identify and classify the WBC at once time which is considered the essential step of leukemia diagnosing. This paper proposed system only based on deep learning algorithms. The modified Faster R-CNN (Faster Region based-Convolutional Neural Networks) algorithm is used to detect and classify WBCs. The system is achieved a high accuracy training on the database tacked directly from microscope which used in [4].

show abstract

Faster R-CNN Model With Momentum Optimizer for RBC and WBC Variants Classification

Cited by 18 publications

References 7 publications

Comprehensive Review of R-CNN and its Variant Architectures

Comprehensive Review of R-CNN and its Variant Architectures

Power control of spectrum sharing for USVs via deep Q-networks

Diagnosing the Leukemia using Faster Region based Convolutional Neural Network

Contact Info

Product

Resources

About