Research on Ear Recognition Based on SSD_MobileNet_v1 Network

Lei, Yanmin; Du, Baowei; Qian, Junru; Feng, Zhibin

doi:10.1109/cac51589.2020.9326541

Cited by 7 publications

(7 citation statements)

References 7 publications

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“…Martinez et al illustrated low-dose CT images for detecting myeloma in the bone marrow [ 16 ]. Xu et al worked on image classification as well as the type and position of objects [ 17 ], VGG [ 18 ], inception [ 19 ], fuzzy logic [ 20 ], Faster R-CNN [ 21 ], SSD [ 19 ], and YOLO [ 11 ], which are all methods for segmentation and object detection. They concluded that for image classification, they had been using a statistical deep learning framework that emphasized object categorization inside the image [ 22 , 23 ].…”

Section: Literature Reviewmentioning

confidence: 99%

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Segmentation and Classification of White Blood Cells Using the UNet

Alharbi

Aravinda²,

Meng

et al. 2022

Contrast Media & Molecular Imaging

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In the bone marrow, plasma cells are made up of B lymphocytes and are a type of WBC. These plasma cells produce antibodies that help to keep bacteria and viruses at bay, thus preventing inflammation. This presents a major challenge for segmenting blood cells, since numerous image processing methods are used before segmentation to enhance image quality. White blood cells can be analyzed by a pathologist with the aid of computer software to identify blood diseases accurately and early. This study proposes a novel model that uses the ResNet and UNet networks to extract features and then segments leukocytes from blood samples. Based on the experimental results, this model appears to perform well, which suggests it is an appropriate tool for the analysis of hematology data. By evaluating the model using three datasets consisting of three different types of WBC, a cross-validation technique was applied to assess it based on the publicly available dataset. The overall segmentation accuracy of the proposed model was around 96%, which proved that the model was better than previous approaches, such as DeepLabV3+ and ResNet-50.

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Section: Literature Reviewmentioning

confidence: 99%

“…Deep learning-based models require a large amount of memory and processing resources for training and testing data. The system is trained repeatedly faster on GPUs than on CPUs [ 11 ]. Deep learning-based segmentation consists of instance segmentation and semantic segmentation.…”

Section: Introductionmentioning

confidence: 99%

Segmentation and Classification of White Blood Cells Using the UNet

Alharbi

Aravinda²,

Meng

et al. 2022

Contrast Media & Molecular Imaging

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“…Here, we learn about the few methods of image classification, which are VGG [ 18 ], inception [ 19 ], and ResNet [ 20 ]. And also, we learn a few methods of segmentation and object detection, which are fuzzy logic [ 21 ], Faster R-CNN [ 22 ], SSD [ 23 ], and YOLO [ 24 ]. They came to the conclusion that they utilized a statistical deep learning framework for image classification, focusing on object categorization inside the image.…”

Section: Literature Reviewmentioning

confidence: 99%

Towards the Segmentation and Classification of White Blood Cell Cancer Using Hybrid Mask-Recurrent Neural Network and Transfer Learning

Das

Islam

Neha

et al. 2021

Contrast Media & Molecular Imaging

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Inside the bone marrow, plasma cells are created, and they are a type of white blood cells. They are made from B lymphocytes. Antigens are produced by plasma cells to combat bacteria and viruses and prevent inflammation and illness. Multiple myeloma is a plasma cell cancer that starts in the bone marrow and causes the formation of abnormal plasma cells. Multiple myeloma is firmly identified by examining bone marrow samples under a microscope for myeloma cells. To diagnose myeloma cells, pathologists have to be very selective. Furthermore, because the ultimate decision is based on human sight and opinion, there is a possibility of error in the result. The nobility of this research is that it provides a computer-assisted technique for recognizing and detecting myeloma cells in bone marrow smears. For recognizing purposes, we have used Mask-Recurrent Convolutional Neural Network, and for detection purposes, Efficient Net B3 has been used. There are already many studies on white blood cell cancer, but very few with both segmentation and classification. We have designed two models. One is for recognizing myeloma cells, and the other is for differentiating them from nonmyeloma cells. Also, a new data set has been made from the multiple myeloma data sets, which has been used in our classification model. This research focuses on hybrid segmentation models and increases the accuracy level of the classification model. Both of our models are trained pretty well, where the Mask-RCNN model gives a mean average precision (mAP) of 93% and the Efficient Net B3 model gives 94.68% accuracy. The result of this research indicates that the Mask-RCNN model can recognize multiple myeloma and Efficient Net B3 can distinguish between myeloma and nonmyeloma cells and beats most of the state of the art in myeloma recognition and detection.

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“…Due to its quick inference speed and high precision, YOLO has been acknowledged as one of the most reliable object detectors [9], [10], [11], [12], [13], [14]. MobileNet [15] is yet another straightforward, effective, and lightweight convolution neural network (CNN) for smartphone applications. Numerous real-world apps, such as object detection, fine-grained classifications, face attributes, and localization, make extensive use of MobileNet [16], [17], [18].…”

Section: Introductionmentioning

confidence: 99%

Comparison of YOLO (V3,V5) and MobileNet-SSD (V1,V2) for Person Identification Using Ear-Biometrics

Hossain,

Anzum,

Akhter

2024

IJCDS

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The ear is a visible organ with a unique structure for each person. As a result, it can be used as a biometric to circumvent the constraints of person identification. Deep learning methods like You Only Look Once (YOLO) and MobileNet have recently significantly aided real-time biometric recognition. As a result, in this paper, we approach identifying a person using YOLOV3, YOLOV5, MobileNet-SSDV1, and MobileNet-SSDV2 deep learning algorithms using their ear biometrics. The used ear biometric is a standard dataset (EarVN1.0 Dataset) from 164 individuals with a total of 27,592 images. We chose 10 people at random, totaling 2057 pictures. Of these, 85% were used for training, 5% for validation, and 10% for testing. The performance of the algorithms is determined based on their accuracy and how smoothly the ear of a person is detected. The training accuracy of the algorithms is thresholded at 99.87%. MobileNet-SSDV1, MobileNet-SSDV2, YOLOV3, and YOLOV5 have testing accuracy that is 88%, 91%, 95%, and 96%, respectively. We concluded that the YOLOV5 model outperforms the others in terms of accuracy and size (16MB) for person identification using ear biometrics.

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Research on Ear Recognition Based on SSD_MobileNet_v1 Network

Cited by 7 publications

References 7 publications

Segmentation and Classification of White Blood Cells Using the UNet

Segmentation and Classification of White Blood Cells Using the UNet

Towards the Segmentation and Classification of White Blood Cell Cancer Using Hybrid Mask-Recurrent Neural Network and Transfer Learning

Comparison of YOLO (V3,V5) and MobileNet-SSD (V1,V2) for Person Identification Using Ear-Biometrics

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