<?xml version="1.0" encoding="UTF-8"?>
<doi_batch version="4.3.0" xmlns="http://www.crossref.org/doi_resources_schema/4.3.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.crossref.org/doi_resources_schema/4.3.0 http://www.crossref.org/schema/deposit/doi_resources4.3.0.xsd">
<head>
<doi_batch_id>0ccc8626-4031-45b7-93f0-eec99b8148ad</doi_batch_id>
<depositor>
<name>beie</name>
<email_address>director@blueeyesintelligence.org</email_address>
</depositor>
</head>
<body>
<doi_citations>
<doi>10.35940/ijrte.C7897.0912323</doi>
<citation_list><citation key="ref0"><unstructured_citation>1. M. Raghu, C. Zhang, J. Kleinberg, and S. Bengio, &quot;Transfusion: Understanding transfer learning for medical imaging,&quot; Advances in neural information processing systems, vol. 32, 2019.</unstructured_citation></citation><citation key="ref1"><doi>10.35119/maio.v1i4.42</doi><unstructured_citation>2. C. Hernandez-Matas, X. Zabulis, A. Triantafyllou, P. Anyfanti, S. Douma, and A. Argyros, &quot;Fire: Fundus image registration dataset,&quot; Journal for Modeling in Opthalmology, Jan. 2017. https://www.maio-journal.com/index.php/MAIO/article/view/42/90https://doi.org/10.35119/maio.v1i4.42</unstructured_citation></citation><citation key="ref2"><unstructured_citation>3. C. Chen, Y. Ren, and C.-C. Kuo, &quot;Global-attributes assisted outdoor scene geometric labeling,&quot; in Feb. 2016, pp. 93-120, ISBN: 978-981-10-0629-6. DOI: 10 . 1007 / 978-981-10-0631-9 5.</unstructured_citation></citation><citation key="ref3"><doi>10.1371/journal.pmed.1002686</doi><unstructured_citation>4. P. Rajpurkar, J. Irvin, R. L. Ball, et al., &quot;Deep learning for chest radiograph diagnosis: A retrospective comparison of the chexnext algorithm to practicing radiologists,&quot; PLoS medicine, vol. 15, no. 11, e1002686, 2018. https://doi.org/10.1371/journal.pmed.1002686</unstructured_citation></citation><citation key="ref4"><doi>10.1016/j.media.2019.03.009</doi><unstructured_citation>5. V. Cheplygina, M. de Bruijne, and J. P. Pluim, &quot;Not-so-supervised: A survey of semi-supervised, multi-instance, and transfer learning in medical image analysis,&quot; Medical image analysis, vol. 54, pp. 280-296, 2019. https://doi.org/10.1016/j.media.2019.03.009</unstructured_citation></citation><citation key="ref5"><doi>10.1155/2022/9580991</doi><unstructured_citation>6. P. Malhotra, S. Gupta, D. Koundal, A. Zaguia, and W. Enbeyle, &quot;Deep neural networks for medical image segmentation,&quot; Journal of Healthcare Engineering, vol. 2022, 2022. https://doi.org/10.1155/2022/9580991</unstructured_citation></citation><citation key="ref6"><doi>10.1016/j.cmpb.2022.106624</doi><unstructured_citation>7. Sk Imran Hossain, Jocelyn de Goër de Herve, Md Shahriar Hassan, Delphine Martineau, Evelina Petrosyan, Violaine Corbin, Jean Beytout, Isabelle Lebert, Jonas Durand, Irene Carravieri, Annick Brun-Jacob, Pascale Frey-Klett, Elisabeth Baux, Céline Cazorla, Carole Eldin, Yves Hansmann, Solene Patrat-Delon, Thierry Prazuck, Alice Raffetin, Pierre Tattevin, Gwenaël Vourc'h, Olivier Lesens, Engelbert Mephu Nguifo, Exploring convolutional neural networks with transfer learning for diagnosing Lyme disease from skin lesion images, Computer Methods and Programs in Biomedicine, Volume 215, 2022,106624, ISSN 01692607,https://doi.org/10.1016/j.cmpb.2022.106624. https://www.sciencedirect.com/science/article/pii/S016 260722000098) https://doi.org/10.1016/j.cmpb.2022.106624</unstructured_citation></citation><citation key="ref7"><doi>10.3390/app13095260</doi><unstructured_citation>8. Zhang L, Bian Y, Jiang P, Zhang F. A Transfer Residual Neural Network Based on ResNet-50 for Detection of Steel Surface Defects. Applied Sciences. 2023;13(9):5260. https://doi.org/10.3390/app13095260.</unstructured_citation></citation><citation key="ref8"><doi>10.1109/ICATMRI51801.2020.9398388</doi><unstructured_citation>9. H. K. Kondaveeti and P. Edupuganti, &quot;Skin Cancer Classification using Transfer Learning,&quot; 2020 IEEE International Conference on Advent Trends in Multidisciplinary Research and Innovation (ICATMRI), Buldhana, India, 2020, pp. 1-4, doi: 10.1109/ICATMRI51801.2020.9398388. https://doi.org/10.1109/ICATMRI51801.2020.9398388</unstructured_citation></citation><citation key="ref9"><doi>10.1109/IBSSC56953.2022.10037374</doi><unstructured_citation>10. A. Shah, &quot;Monkeypox Skin Lesion Classification Using Transfer Learning Approach,&quot; 2022 IEEE Bombay Section Signature Conference (IBSSC), Mumbai, India, 2022, pp. 1-5, doi: 10.1109/IBSSC56953.2022.10037374. https://doi.org/10.1109/IBSSC56953.2022.10037374</unstructured_citation></citation><citation key="ref10"><doi>10.3390/ijerph20054422</doi><unstructured_citation>11. Jaradat AS, Al Mamlook RE, Almakayeel N, Alharbe N, Almuflih AS, Nasayreh A, Gharaibeh H, Gharaibeh M, Gharaibeh A, Bzizi H. Automated Monkeypox Skin Lesion Detection Using Deep Learning and Transfer Learning Techniques. Int J Environ Res Public Health. 2023 Mar 1;20(5):4422. doi: 10.3390/ijerph20054422. PMID: 36901430; PMCID: PMC10001976. https://doi.org/10.3390/ijerph20054422</unstructured_citation></citation><citation key="ref11"><doi>10.1145/3331453.3361658</doi><unstructured_citation>12. Qian Xiang, Xiaodan Wang, Rui Li, Guoling Zhang, Jie Lai, Qingshuang Hu, CSAE '19: Proceedings of the 3rd International Conference on Computer Science and Application EngineeringOctober 2019Article No.: 121Pages 1-7https://doi.org/10.1145/3331453.3361658. https://doi.org/10.1145/3331453.3361658</unstructured_citation></citation><citation key="ref12"><doi>10.3390/buildings13020572</doi><unstructured_citation>13. Qin, Y.; Tang, Q.; Xin, J.; Yang, C.; Zhang, Z.; Yang, X. A Rapid Identification Technique of Moving Loads Based on MobileNetV2 and Transfer Learning. Buildings 2023, 13, 572. https://doi.org/10.3390/buildings13020572.</unstructured_citation></citation><citation key="ref13"><doi>10.3390/horticulturae8121119</doi><unstructured_citation>14. Li T, Huang H, Peng Y, Zhou H, Hu H, Liu M. Quality Grading Algorithm of Oudemansiella raphanipes Based on Transfer Learning and MobileNetV2. Horticulturae. 2022; 8(12):1119. https://doi.org/10.3390/horticulturae8121119.</unstructured_citation></citation><citation key="ref14"><doi>10.1109/INCET49848.2020.9154014</doi><unstructured_citation>15. R. Patel and A. Chaware, &quot;Transfer Learning with Fine-Tuned MobileNetV2 for Diabetic Retinopathy,&quot; 2020 International Conference for Emerging Technology (INCET), Belgaum, India, 2020, pp. 1-4, doi: 10.1109/INCET49848.2020.9154014. https://doi.org/10.1109/INCET49848.2020.9154014</unstructured_citation></citation><citation key="ref15"><unstructured_citation>16. S. Dubey, Alzheimer's Dataset ( 4 class of Images), https: //www.kaggle.com/datasets/tourist55/alzheimers-dataset--4-class-of-images, 2020.</unstructured_citation></citation><citation key="ref16"><unstructured_citation>17. M. Hany, Chest CT-Scan images Dataset), https://www.kaggle.com/datasets/mohamedhanyyy/ches-ctscan-images9. P. Raikote, Covid-19 Image Dataset, https://www.kaggle.com / datasets / pranavraikokte / covid19image-dataset?datasetId=627146&amp;sortBy=voteCount, 2020</unstructured_citation></citation></citation_list>
</doi_citations>
</body>
</doi_batch>
