Relevant bibliographies by topics / Efficientnetv2s

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Academic literature on the topic 'Efficientnetv2s'

Author: Grafiati
Published: 7 June 2025
Last updated: 16 July 2025

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Journal articles on the topic "Efficientnetv2s"

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Abioye, Oluwasegun A., Abraham E. Evwiekpaefe, and Awujoola J. Awujoola. "PERFORMANCE EVALUATION OF EFFICIENTNETV2 MODELS ON THE CLASSIFICATION OF HISTOPATHOLOGICAL BENIGN BREAST CANCER IMAGES." Science Journal of University of Zakho 12, no. 2 (2024): 208–14. http://dx.doi.org/10.25271/sjuoz.2024.12.2.1261.

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In the field of breast cancer diagnosis, the precise classification of benign images plays a pivotal role in ensuring effective patient care. This research undertakes a detailed examination of EfficientNetV2 models, specifically focusing on their ability to discern benign histopathology breast cancer images. The dataset were carefully curated to include diverse benign cases such as adenosis, fibroadenoma, phyllodes_tumor, and tubular_adenoma of image level for 40X magnification factor underwent thorough preprocessing before being divided into training and testing sets. Various variants of the
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Manolescu, Denis, Neil Buckley, and Emanuele Lindo Secco. "Machine Learning Models for Probability Classification in Spectrographic EEG Seizures Dataset." WSEAS TRANSACTIONS ON BIOLOGY AND BIOMEDICINE 21 (September 23, 2024): 260–71. http://dx.doi.org/10.37394/23208.2024.21.27.

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The examination of brain signals, namely the Electroencephalogram (EEG) signals, is an approach to possibly detect seizures of the brain. Due to the nature of these signals, deep learning techniques have offered the opportunity to perform automatic or semi-automatic analysis which could support decision and therapeutical approaches. This paper focuses on the possibility of classifying EEG seizure using convolutional layers (namely EfficientNetV2 architectures, i.e., EfficientNetV2S and EfficientNetV2B2), Long Short-Term Memory (LSTM) units, and fine-tuned mechanisms of attention. We use these
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Yan, Xingchen. "Galaxy recognition based on improved efficientNetV2S." Applied and Computational Engineering 21, no. 1 (2023): 134–42. http://dx.doi.org/10.54254/2755-2721/21/20231132.

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In order to identify effective metrics that can accurately duplicate the probability distributions resulting from human classifications, this paper analyzes an improved approach for galaxy morphologies classification. At the present stage, this field still faces the problem of insufficient quality and quantity of image data, low accuracy of computer recognition and weak generalization ability of the model. From the previous research, Convolution Neural Network (CNN) can be a valid technique to complete this task but usually spends a large time and space complexity. For the purpose of increasin
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Purnomo, Juanda Gilang, Sigit Birowo, and Muhammad Akbar Maulana. "Identifikasi Malaria Pada Citra Darah Dengan Convolutional Neural Network." bit-Tech 7, no. 2 (2024): 406–12. https://doi.org/10.32877/bt.v7i2.1828.

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Penelitian ini membahas penerapan model pretrained EfficientNetV2S dan ConvNeXtBase untuk mendeteksi keberadaan parasit malaria dalam citra darah mikroskopis. Dataset yang digunakan terdiri dari 27.588 citra darah manusia, yang terbagi menjadi dua kategori, yaitu Parasitized dan Uninfected. Model dilatih menggunakan dua pengaturan learning rate: minimum (0.0001) dan maksimum (0.001), untuk membandingkan performa dan stabilitasnya dalam proses klasifikasi. Hasil penelitian menunjukkan bahwa model EfficientNetV2S dengan learning rate minimum mencapai akurasi validasi tertinggi sebesar 96,58%, me
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Nurhopipah, Ade, Jali Suhaman, and Anan Widianto. "Exploring Pre-Trained Model and Language Model for Translating Image to Bahasa." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 17, no. 4 (2023): 347. http://dx.doi.org/10.22146/ijccs.76389.

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In the last decade, there have been significant developments in Image Caption Generation research to translate images into English descriptions. This task has also been conducted to produce texts in non-English, including Bahasa. However, the references in this study are still limited, so exploration opportunities are open widely. This paper presents comparative research by examining several state-of-the-art Deep Learning algorithms to extract images and generate their descriptions in Bahasa. We extracted images using three pre-trained models, namely InceptionV3, Xception, and EfficientNetV2S.
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Efendi, Ilham Julian, Jabir Muktabir, Khaerunni Salsa Billah, Muhammad Vannes Al Qadri, Wa Ode Asriyani, and Amar Adi Ismoyo. "DIAGNOCAR: PENDETEKSIAN OTOMATIS KERUSAKAN MOBIL MENGGUNAKAN DEEP LEARNING BERBASIS CITRA LEWAT PERANGKAT MOBILE." Simtek : jurnal sistem informasi dan teknik komputer 10, no. 1 (2025): 199–205. https://doi.org/10.51876/simtek.v10i1.1547.

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Penelitian ini bertujuan mengembangkan sistem klasifikasi otomatis berbasis arsitektur Convolutional Neural Network untuk mendeteksi jenis kerusakan pada mobil melalui data citra. Sistem ini diharapkan dapat mempercepat dan meningkatkan akurasi proses penilaian kerusakan pada klaim asuransi dan perbaikan kendaraan. Lima arsitektur telah diimplementasikan, yaitu MobileNetV2, EfficientNetV2S, NASNetMobile, ResNet50, dan model konvolusional yang dirancang sendiri, dengan menggunakan dataset berjumlah 1.594 citra yang terbagi dalam enam kelas kerusakan. Proses pelatihan mencakup praproses data, au
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Grd, Petra, Igor Tomičić, and Ena Barčić. "Transfer Learning with EfficientNetV2S for Automatic Face Shape Classification." JUCS - Journal of Universal Computer Science 30, no. 2 (2024): 153–78. http://dx.doi.org/10.3897/jucs.104490.

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The classification of human face shapes, a pivotal aspect of one’s appearance, plays a crucial role in diverse fields like beauty, cosmetics, healthcare, and security. In this paper, we present a multi-step methodology for face shape classification, harnessing the potential of transfer learning and a pretrained EfficientNetV2S neural network. Our approach comprises key phases, including preprocessing, augmentation, training, and testing, ensuring a comprehensive and reliable solution. The preprocessing step involves precise face detection, cropping, and image scaling, laying a solid
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Grd, Petra, Igor Tomičić, and Ena Barčić. "Transfer Learning with EfficientNetV2S for Automatic Face Shape Classification." JUCS - Journal of Universal Computer Science 30, no. (2) (2024): 153–78. https://doi.org/10.3897/jucs.104490.

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The classification of human face shapes, a pivotal aspect of one's appearance, plays a crucial role in diverse fields like beauty, cosmetics, healthcare, and security. In this paper, we present a multi-step methodology for face shape classification, harnessing the potential of transfer learning and a pretrained EfficientNetV2S neural network. Our approach comprises key phases, including preprocessing, augmentation, training, and testing, ensuring a comprehensive and reliable solution. The preprocessing step involves precise face detection, cropping, and image scaling, laying a solid foundation
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Shehzad, Khurram, Tan Zhenhua, Shifa Shoukat, et al. "A Deep-Ensemble-Learning-Based Approach for Skin Cancer Diagnosis." Electronics 12, no. 6 (2023): 1342. http://dx.doi.org/10.3390/electronics12061342.

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Skin cancer is one of the widespread diseases among existing cancer types. More importantly, the detection of lesions in early diagnosis has tremendously attracted researchers’ attention. Thus, artificial intelligence (AI)-based techniques have supported the early diagnosis of skin cancer by investigating deep-learning-based convolutional neural networks (CNN). However, the current methods remain challenging in detecting melanoma in dermoscopic images. Therefore, in this paper, we propose an ensemble model that uses the vision of both EfficientNetV2S and Swin-Transformer models to detect the e
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Liana, Verianti, Rizal Arifiandika, Bagas Rohmatulloh, et al. "Enzyme dosage detection to degrade feathers in edible bird’s nests: A comparative convolutional neural networks study." Advances in Food Science, Sustainable Agriculture and Agroindustrial Engineering 6, no. 4 (2023): 382–98. http://dx.doi.org/10.21776/ub.afssaae.2023.006.04.6.

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Edible Bird’s Nest (EBN), a costly food product made from swiftlet’s saliva, has encountered a longstanding problem of plucking the swiftlet’s feather from the nests. The destructive and inefficient manual process of plucking the feathers can be substituted with a serine protease enzyme alternative. Accurate detection of enzyme dosage is crucial for ensuring efficient feather degradation with cost-effective enzyme usage. This research employed the transfer learning method using pretrained Convolutional Neural Networks (Pt-CNN) to detect enzyme dosage based on EBN’s images. This study aimed to
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Book chapters on the topic "Efficientnetv2s"

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Bhatoa, Jai, Stian Knoll Johansen, and Rashmi Gupta. "Optimizing Alzheimer’s Detection: A Comparative Analysis of Pre-processing Techniques and FreeSurfer Integration with EfficientNetV2S." In IFIP Advances in Information and Communication Technology. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-96235-6_16.

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Pillai, Manju Balakrishna, and Jyothisha J. Nair. "Nuclei Segmentation Using UNet with EfficientNetV2 as Encoder." In ICT Systems and Sustainability. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-5221-0_57.

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Xin, Yiran, Yunqi Tang, and Zuhe Yang. "Shoe Print Retrieval Algorithm Based on Improved EfficientnetV2." In Biometric Recognition. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-20233-9_45.

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Gordienko, Yuri, Nikita Gordienko, Roman Fedunyshyn, and Sergii Stirenko. "Multibackbone Ensembling of EfficientNetV2 Architecture for Image Classification." In Algorithms for Intelligent Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-1452-3_5.

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Kumar, Atishek, Rishabh Jain, and Rudresh Dwivedi. "Weed Detection in Crops Using Lightweight EfficientNets." In Communication and Intelligent Systems. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2100-3_13.

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Che, Anan, Cheng Wang, Kun Lu, Tao Tao, Wenyan Wang, and Bing Wang. "Multi-label Classification for Concrete Defects Based on EfficientNetV2." In Lecture Notes in Computer Science. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-5591-2_4.

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Duc, Hoa Le, Tin Tang Minh, Khanh Vo Hong, and Huong Luong Hoang. "84 Birds Classification Using Transfer Learning and EfficientNetB2." In Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-8069-5_50.

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Ni, Jiawei, Bing Wang, Kun Lu, et al. "Multiple Classification Network of Concrete Defects Based on Improved EfficientNetV2." In Lecture Notes in Computer Science. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-4742-3_52.

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Xu, Xuebin, Dehua Liu, Muyu Wang, and Meng Lei. "Application of End-To-End EfficientNetV2 in Diabetic Retinopathy Grading." In Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-20738-9_22.

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Kaur, Sukhpreet, Aryan Budukh, Umerkhan Golandaz, and Prajakta Dhamdhere. "Precision fatigue detection among individuals through leveraged fine-tuned EfficientNetV2." In Intelligent Computing and Communication Techniques. CRC Press, 2025. https://doi.org/10.1201/9781003530190-79.

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Conference papers on the topic "Efficientnetv2s"

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Khullar, Vikas, Rakesh Ahuja, Vikas Solanki, and Amit Chaudhary. "Real Fake Image Classification using Explainable EfficientNetV2S: A Comparative Analysis." In 2024 International Conference on Electrical Electronics and Computing Technologies (ICEECT). IEEE, 2024. http://dx.doi.org/10.1109/iceect61758.2024.10739256.

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Fu, Yunfang, Qian Qi, Bin Wu, and Long Zhang. "Enhancing EfficientNetV2 for Adult Age Estimation of Forensic Pelvis." In 2024 IEEE 17th International Conference on Signal Processing (ICSP). IEEE, 2024. https://doi.org/10.1109/icsp62129.2024.10846408.

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Singh, Retinderdeep, Neha Sharma, Kapil Rajput, and Mukesh Kumar. "Deep Fake Identification Made Efficient: The Power of EfficientNetV2-B1." In 2024 4th Asian Conference on Innovation in Technology (ASIANCON). IEEE, 2024. https://doi.org/10.1109/asiancon62057.2024.10838210.

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Singh, Retinderdeep, Neha Sharma, Kapil Rajput, and Mukesh Kumar. "Improving Medical Diagnostics with EfficientNetV2-B1 in Blood Cell Analysis." In 2024 4th Asian Conference on Innovation in Technology (ASIANCON). IEEE, 2024. https://doi.org/10.1109/asiancon62057.2024.10838050.

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Singla, Seerat, and Rupesh Gupta. "Deep Learning based Bean Leaf Lesion Classification utilizing EfficientNetV2-S." In 2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). IEEE, 2024. http://dx.doi.org/10.1109/i-smac61858.2024.10714612.

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Konuk, Mehmet Emin, and Erdal Kılıç. "EfficientFER: EfficientNetv2 Based Deep Learning Approach for Facial Expression Recognition." In 2025 7th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (ICHORA). IEEE, 2025. https://doi.org/10.1109/ichora65333.2025.11017006.

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Roseline, S. Abijah, S. K. Md Hussain Basha, and R. Parkavi. "Intelligent Brain Tumor Detection using Fine-Tuned EfficientNets." In 2024 Third International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN). IEEE, 2024. http://dx.doi.org/10.1109/icstsn61422.2024.10670846.

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Tafala, Idriss, Fatima-Ezzahraa Ben-Bouazza, Aymane Edder, Oumaima Manchadi, Mehdi Et-Taoussi, and Bassma Jioudi. "EfficientNetV2 and Attention Mechanisms for the automated detection of Cephalometric landmarks." In 2024 International Conference on Intelligent Systems and Computer Vision (ISCV). IEEE, 2024. http://dx.doi.org/10.1109/iscv60512.2024.10620094.

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Li, Yongkang, Yin Pan, Yubo Li, and Zhi Zeng. "Binary Classification of Diabetic Foot Thermograms Based on HD-EfficientNetV2 Network." In 2024 6th International Conference on Electronic Engineering and Informatics (EEI). IEEE, 2024. http://dx.doi.org/10.1109/eei63073.2024.10695995.

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Shah, Priyanka, Dhiraj Keshari, K. Sankar, R. Sugumar, Chandrasekar Venkatachalam, and Shanmugavalli Venkatachalam. "Advancements in Automated Classification of Knee Osteoarthritis Severity using EfficientNetV2-S." In 2024 5th International Conference on Smart Electronics and Communication (ICOSEC). IEEE, 2024. http://dx.doi.org/10.1109/icosec61587.2024.10722711.

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Journal articles
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