SqueezeNet and Fusion Network-Based Accurate Fast Fully Convolutional Network for Hand Detection and Gesture Recognition

Qiang, Baohua; Zhai, Yijie; Zhou, Mingliang; Yang, Xianyi; Peng, Bo; Wang, Yufeng; Pang, Yuanchao

doi:10.1109/access.2021.3079337

Cited by 30 publications

(13 citation statements)

References 47 publications

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“…In addition, a residual is considered to the SqueezeNet network structure for the deconvolutional network to compile with the high and low feature integration hand process while the detecting and recognizing the hand motions have been taking place at the signal convoluted layer with different scale levels for cost reduction and accuracy enhancement. The SqueezeNet network employs convolutional separation by substituting the standard convolution of 3 × 3 with a convolutional kernel of 1 × 1 [66]. SqueezeNet was designed to reduce the number of model parameters and the model size by Iandola et al.…”

Section: Methods and Designmentioning

confidence: 99%

Optimizing chest tuberculosis image classification with oversampling and transfer learning

Alqahtani,

Abu Al‐Haija,

Alsulami

et al. 2023

IET Image Processing

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Tuberculosis (TB) is an extremely contagious illness caused by Mycobacterium tuberculosis. Chest tuberculosis classification is conducted based on a deep convolutional neural network architecture. In this research, a pre‐trained network is utilized to demonstrate the advantage of using the oversampling technique on the classification of TB and compare results with recent research that used the same dataset. Therefore, the dataset consists of 3500 uninfected TB cases and 700 infected with TB. This paper circumvents the imbalance by using the oversampling technique in X‐ray TB images to be fed into several pre‐trained networks for TB classification. The oversampling technique is crucial in enhancing the performance of TB classification compared with other pre‐trained models reported here. Inceptionv3 shows a promising result compared to other pre‐trained models; it achieves 99.94% accuracy, 99.88% precision, 100% recall, and 99.94% F1‐Score.

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Section: Methods and Designmentioning

confidence: 99%

Optimizing chest tuberculosis image classification with oversampling and transfer learning

Alqahtani,

Abu Al‐Haija,

Alsulami

et al. 2023

IET Image Processing

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show abstract

“…However, this is a different form of comparative analysis, where the outcome of proposed system (performance parameters with segmentation) is further subjected to 7 version of neural network viz. Resnet 18 [32], Resnet 50 [33], Resnet101 [34], VGG19 [35], Densenet201 [36], Squeezenet [37], and Mobilenet [38].…”

Section: Extensive Analysis With Different Versions Of Cnnmentioning

confidence: 99%

An Add-on CNN based Model for the Detection of Tuberculosis using Chest X-ray Images

Roopa¹,

Mamatha²

2023

IJACSA

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MachineLearning has been potentially contributing towards smart diagnosis in the medical domain for more than a decade with a target towards achieving higher accuracy in detection and classification. However, from the perspective of medical image processing, the contribution of machine learning towards segmentation is not been much to find in recent times. The proposed study considers a use case of Tuberculosis detection and classification from chest x-rays where a unique machine learning approach of Convolution Neural Network is adopted for segmentation of lung images from CXR. A computational framework is developed that performs segmentation, feature extraction, detection, and classification. The proposed system's study outcome is analyzed with and without segmentation over existing machine learning models to exhibit 99.85% accuracy, which is the highest score to date in contrast to existing approaches found in the literature. The study outcome based on the comparative analysis exhibits the effectiveness of the proposed system.

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“…SqueezeNet was built based on the CNN concept, with a few modifications. For SqueezeNet, the elements in the convolution and pooling layers shown in Figure 1 are mostly replaced with fire module blocks [22]. A fire module comprises squeeze and expand layers, as shown in Figure 2.…”

Section: Squeezenetmentioning

confidence: 99%

Footprint biometric authentication using SqueezeNet

Safie

Ramli

2023

IJEECS

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Biometric authentication is a process of identity verification once an identity is claimed by an individual. It uses unique features on the human body. Footprints are a new biometric feature that has sparked interest among researchers, as this feature is universal, easy to extract and has not changed throughout time. The focus of researchers in this field is to improve the recognition rate. Various techniques have been developed for this purpose, but the accuracy percentage is at 98% with an equal error rate (EER) of 6.1%. This paper proposes the use of a new technique called SqueezeNet in classifying footprint images. SqueezeNet belongs to the convolutional neural network (CNN) family. In this study, 300 footprint images were used from 15 individuals. The 70% of these images were used to train the proposed SqueezeNet network, while the rest were used for testing. At the end of this simulation, SqueezeNet has achieved an accuracy of 98.67% with an EER of 2.1%.

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SqueezeNet and Fusion Network-Based Accurate Fast Fully Convolutional Network for Hand Detection and Gesture Recognition

Cited by 30 publications

References 47 publications

Optimizing chest tuberculosis image classification with oversampling and transfer learning

Optimizing chest tuberculosis image classification with oversampling and transfer learning

An Add-on CNN based Model for the Detection of Tuberculosis using Chest X-ray Images

Footprint biometric authentication using SqueezeNet

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