An Improved EfficientNetV2 Model Based on Visual Attention Mechanism: Application to Identification of Cassava Disease

Ye, Yuanbo; Zhou, Houkui; Yu, Huimin; Hu, Rong; Zhang, Guangqun; Hu, Junguo; He, Tao

doi:10.1155/2022/1569911

Cited by 13 publications

(4 citation statements)

References 25 publications

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“…The Efficient-Net V2L network is an extension of the original Efficient-Net architecture, designed specifically for image recognition tasks. It is renowned for its ability to learn complex and distinctive features from input images and is designed to efficiently handle large-scale image datasets [28].…”

Section: Deep Learning Algorithmsmentioning

confidence: 99%

An Integrated Multimodal Deep Learning Framework for Accurate Skin Disease Classification

Hamida,

Lamrani,

Bouqentar

et al. 2024

Int. J. Onl. Eng.

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In order to effectively treat skin diseases, an accurate and prompt diagnosis is required. In this article, a novel method for classifying skin disorders using a multimodal classifier is presented. The proposed classifier utilizes multiple information sources to enhance the accuracy of disease classification. It incorporates images of skin lesions and patient-specific data. The multimodal classifier simultaneously classifies diseases by combining image and structured data inputs. The effectiveness of the proposed classifier was evaluated using the ISIC 2018 dataset, which includes images and clinical data for seven categories of skin diseases. The results indicate that the proposed model outperforms conventional single-modal and single-task classifiers, achieving an accuracy of 98.66% for image classification and 94.40% for clinical data classification. In addition, we compare the performance of the proposed model with that of other methodologies, demonstrating its superiority. Despite yielding promising results, the proposed method has limitations in terms of data requirements and generalizability. Future research directions include incorporating additional information sources, investigating genetic data integration, and applying the method to various medical conditions. This study illustrates the potential of integrating multimodal techniques with transfer learning in deep neural networks to enhance the classification accuracy of cutaneous diseases.

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Section: Deep Learning Algorithmsmentioning

confidence: 99%

An Integrated Multimodal Deep Learning Framework for Accurate Skin Disease Classification

Hamida,

Lamrani,

Bouqentar

et al. 2024

Int. J. Onl. Eng.

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“…The proposed model PFDI and existing model are compared based on performance measuring parameters precision (PC), Recall (RC), F1 score (FS) and accuracy (Acy) [39].…”

Section: Performance Measuring Parametersmentioning

confidence: 99%

PFDI: A Precise Fruit disease Identification Model based on Context Data Fusion with Faster-CNN in Edge Computing Environment

Dhiman¹,

Poongodi

Lilhore

et al. 2023

Preprint

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Fruits have a significant impact on everyday living i.e., citrus fruits. Numerous fruits have a solid nutritious value and are packed with multivitamins and trace components. Citrus fruits are delicate, so they are susceptible to many diseases and infections. Many researchers have suggested various deep learning and machine learning based fruit disease detection and classification models. In this research we are presenting precise fruit disease identification (PFDI) model based on context data fusion with Faster-CNN in edge computing environment. The goal is to develop a precise, efficient, and trustable fruit disease detection model, which is a critical component of an autonomous food production in robotic edge platform. This research examines and explores four different diseases of citrus fruits using CNN deep learning models to be adopted as edge computing solution. Identification of citrus diseases namely cankers black spot, greening, scab, melanose, and healthy citrus fruits are implemented using the proposed sequential model without pruning, with pruning having different sparsity level followed by post quantization. Through transfer learning method, we optimize this model for the assignment of fruit disease detection employing visuals from two patterns: Near-infrared (NIFR) and RGB. For integrating multi-model (NIFR and RGB) facts, early and late data fusion techniques are evaluated. The accuracy obtained from the proposed model for the canker disease is 97%,scab 95%, melanose 99% ,Greening 97%,Black spot 97% and for healthy 97%. In this paper we compared and evaluated the results of proposed model with the sparsity levels of 50–80%, 60–90%, 70–90%, 80–90% pruning and also obtained the results of post-quantization on each level. The results show that the size of the model with 60–90% pruning can be counteracted to the 47.64 of the baseline model without significant loss of accuracy. Moreover, post quantization can further reduces the of 60–90% pruning from 28.16 to 8.72. In addition to enhanced precision, the above initiative is much faster to implement for new fruits diseases because it needs bounding box annotation (BBA) instead of pixel-level annotation (PLA).

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“…E cientnetV2, as a newly proposed classi cation network, increases the network width, depth, and resolution to improve the performance [27,28]. It has been used in plant disease detection [29,30], mechanical fault diagnosis [31], and some other elds [32,33].…”

Section: Introductionmentioning

confidence: 99%

Damage identification of wire rope under noise background via Light- EfficientNetV2 and Magnetic flux leakage image

Huang

Wang

Yang

et al. 2023

Preprint

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Magnetic flux leakage (MFL) testing, non-destructive testing, can prevent some major accidents of hoist equipment by identifying the damage of wire ropes. However, in harsh working conditions such as mines and oil wells, the inevitable vibration and swing of wire rope will generate noise and interfere with the MFL signal, which makes us difficult to identify the damage. As a classification network, Convolutional neural network (CNN) is positive in recognition accuracy and noise resistance, but it hardly uses in wire rope damage classification. To improve the accuracy of wire rope damage identification under noise background, we propose a method of wire rope damage identification via Light-EfficientNetV2 and MFL image. First, the MFL signal is segmented and rearranged to form the MFL image, and then the image is classified by Light-EfficientNetV2. To improve the classification efficiency, we reduce the layers of EfficientNetV2 to make it lighter. Finally, the availability of this method is proved by the validation set. Compared with four neural networks, the accuracy is the highest. Moreover, as the noise increased, the accuracy of Light-EfficientNetV2 is higher than EfficientNetV2, which has application value in the wire rope damage identification under noise background.

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An Improved EfficientNetV2 Model Based on Visual Attention Mechanism: Application to Identification of Cassava Disease

Cited by 13 publications

References 25 publications

An Integrated Multimodal Deep Learning Framework for Accurate Skin Disease Classification

An Integrated Multimodal Deep Learning Framework for Accurate Skin Disease Classification

PFDI: A Precise Fruit disease Identification Model based on Context Data Fusion with Faster-CNN in Edge Computing Environment

Damage identification of wire rope under noise background via Light- EfficientNetV2 and Magnetic flux leakage image

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