A new lightweight network based on MobileNetV3

doi:10.3837/tiis.2022.01.001

Cited by 18 publications

(11 citation statements)

References 24 publications

(35 reference statements)

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“…The MobileNetV2 model comprises 88 discrete levels. Both convolutional and fully linked layers are present [ 65 ]. About 3.5 million parameters make up the MobileNetV2 model.…”

Section: Methodsmentioning

confidence: 99%

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Enhancing Skin Cancer Detection and Classification in Dermoscopic Images through Concatenated MobileNetV2 and Xception Models

Ogundokun,

Li,

Babatunde

et al. 2023

Bioengineering

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One of the most promising research initiatives in the healthcare field is focused on the rising incidence of skin cancer worldwide and improving early discovery methods for the disease. The most significant factor in the fatalities caused by skin cancer is the late identification of the disease. The likelihood of human survival may be significantly improved by performing an early diagnosis followed by appropriate therapy. It is not a simple process to extract the elements from the photographs of the tumors that may be used for the prospective identification of skin cancer. Several deep learning models are widely used to extract efficient features for a skin cancer diagnosis; nevertheless, the literature demonstrates that there is still room for additional improvements in various performance metrics. This study proposes a hybrid deep convolutional neural network architecture for identifying skin cancer by adding two main heuristics. These include Xception and MobileNetV2 models. Data augmentation was introduced to balance the dataset, and the transfer learning technique was utilized to resolve the challenges of the absence of labeled datasets. It has been detected that the suggested method of employing Xception in conjunction with MobileNetV2 attains the most excellent performance, particularly concerning the dataset that was evaluated: specifically, it produced 97.56% accuracy, 97.00% area under the curve, 100% sensitivity, 93.33% precision, 96.55% F1 score, and 0.0370 false favorable rates. This research has implications for clinical practice and public health, offering a valuable tool for dermatologists and healthcare professionals in their fight against skin cancer.

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“…The MobileNetV2 model comprises 88 discrete levels. Both convolutional and fully linked layers are present [ 65 ]. About 3.5 million parameters make up the MobileNetV2 model.…”

Section: Methodsmentioning

confidence: 99%

“…Most systems [ 61 , 62 , 63 , 64 ] extract characteristics from images using various image processing techniques and then input those features into a categorization method [ 65 ]. Khan et al [ 66 ] provided a strategy for identifying and categorizing melanoma and nevi.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Skin Cancer Detection and Classification in Dermoscopic Images through Concatenated MobileNetV2 and Xception Models

Ogundokun,

Li,

Babatunde

et al. 2023

Bioengineering

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show abstract

“…Compared with the traditional convolutional neural network, MobileNet has the advantages of fewer parameters and lower delay. At present, MobileNet series networks include MobileNetv1, MobileNetv2 ( Huu et al., 2022 ; Młodzianowski, 2022 ) and MobileNetv3 ( Howard et al., 2019 ; Hussain et al., 2021 ; Zhao and Wang, 2022 ; Zhao et al., 2022 ). MobileNetv3 is Google’s new invention after MobileNetv2, and its main improvement is to add SE-net after the deep separable convolution in MobileNetv2, which automatically obtains the importance of each feature channel by learning, and suppresses some feature information that is not useful for the current task.…”

Section: Related Workmentioning

confidence: 99%

A lightweight method for maize seed defects identification based on Convolutional Block Attention Module

Li,

Chen,

Jing

et al. 2023

Front. Plant Sci.

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Maize is widely cultivated and planted all over the world, which is one of the main food resources. Accurately identifying the defect of maize seeds is of great significance in both food safety and agricultural production. In recent years, methods based on deep learning have performed well in image processing, but their potential in the identification of maize seed defects has not been fully realized. Therefore, in this paper, a lightweight and effective network for maize seed defect identification is proposed. In the proposed network, the Convolutional Block Attention Module (CBAM) was integrated into the pretrained MobileNetv3 network for extracting important features in the channel and spatial domain. In this way, the network can be focused on useful feature information, and making it easier to converge. To verify the effectiveness of the proposed network, a total of 12784 images was collected, and 7 defect types were defined. Compared with other popular pretrained models, the proposed network converges with the least number of iterations and achieves the true positive rate is 93.14% and the false positive rate is 1.14%.

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“…The business sector is growing more aware of social, labor, and human rights issues as supply chains become more complicated. The ESG score is determined by considering several factors for an environmental pillar, social pillar, and governance pillar (Choi et al, 2021;Yang et al, 2022;Zhao and Wang 2022). Greenhouse gas emissions, deforestation, waste material and pollution, climate change, and resource depletion are all factors that impact a company's environmental pillar.…”

Section: Environment Social and Governance (Esg)mentioning

confidence: 99%

Applying artificial intelligence techniques for predicting the environment, social, and governance (ESG) pillar score based on balance sheet and income statement data: A case of non-financial companies of USA, UK, and Germany

Raza

Khan

Su’ud

et al. 2022

Front. Environ. Sci.

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Due to globalization, environment, social, and governance (ESG) issues have gained importance over the last few decades. ESG is a worldwide issue, which clarifies that organizations throughout the world are lacking in contribution to the environment, society, and corporate governance characteristics for sustainable development. The problem of ESG spread over all stakeholders needs to be addressed. In this regard, rating agencies also have a close eye on ESG issues and have developed the methodology of score that aims to provide disclosure on ESG metrics which, in return, help investors and asset managers better differentiate between responsible and irresponsible companies. The ESG score has become an important tool among asset managers but is highly questioned in terms of reliability. The study objective was to develop machine learning algorithms to assess how balance sheet and income statement data impact the Thomson Reuters ESG score for non-financial public companies of USA, UK, and Germany from 2008 to 2020. In addition, the study also has an objective to assess which machine learning (ML) algorithm better predicts the ESG score using structural data, that is, return on assets (ROA), return on equity (ROE), earning per share (EPS), earnings before interest and taxes (EBIT), dividend yield, and net sales. The results concluded that balance sheet and income statement data are critical in explaining the ESG score, and the ANN algorithm outperforms with minimum RMSE and MAE values. All in all, the results of the study, based on the concept of artificial intelligence, bring suggestion for improvement to regulatory bodies, researchers, academia, practitioners, publicly listed companies around the globe, and last but not the least to the US, UK, and Germany markets. Moreover, it also provides suggestions for up-to-date compliance of ESG-relevant activities for boosting the firm performance.

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A new lightweight network based on MobileNetV3

Cited by 18 publications

References 24 publications

Enhancing Skin Cancer Detection and Classification in Dermoscopic Images through Concatenated MobileNetV2 and Xception Models

Enhancing Skin Cancer Detection and Classification in Dermoscopic Images through Concatenated MobileNetV2 and Xception Models

A lightweight method for maize seed defects identification based on Convolutional Block Attention Module

Applying artificial intelligence techniques for predicting the environment, social, and governance (ESG) pillar score based on balance sheet and income statement data: A case of non-financial companies of USA, UK, and Germany

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