{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T00:32:16Z","timestamp":1775176336827,"version":"3.50.1"},"reference-count":25,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T00:00:00Z","timestamp":1755216000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Symbiosis Research Fund (RSF) of Symbiosis International (Deemed University), Pune, Maharashtra, India"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>The increasing demand for accessible and efficient machine learning solutions has led to the development of the Adaptive Learning Framework (ALF) for multi-class, single-label image classification. Unlike existing low-code tools, ALF integrates multiple transfer learning backbones with a guided, adaptive workflow that empowers non-technical users to create custom classification models without specialized expertise. It employs pre-trained models from TensorFlow Hub to significantly reduce computational costs and training times while maintaining high accuracy. The platform\u2019s User Interface (UI), built using Streamlit, enables intuitive operations, such as dataset upload, class definition, and model training, without coding requirements. This research focuses on small-scale image datasets to demonstrate ALF\u2019s accessibility and ease of use. Evaluation metrics highlight the superior performance of transfer learning approaches, with the InceptionV2 model architecture achieving the highest accuracy. By bridging the gap between complex deep learning methods and real-world usability, ALF addresses practical needs across fields like education and industry.<\/jats:p>","DOI":"10.3390\/fi17080370","type":"journal-article","created":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T08:39:07Z","timestamp":1755247147000},"page":"370","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Adaptive and User-Friendly Framework for Image Classification with Transfer Learning Models"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-8220-0085","authenticated-orcid":false,"given":"Manan","family":"Khatri","sequence":"first","affiliation":[{"name":"Symbiosis Institute of Technology (SIT), Pune Campus, Symbiosis International (Deemed University) (SIU), Lavale, Pune 412115, Maharashtra, India"}]},{"given":"Manmita","family":"Sahoo","sequence":"additional","affiliation":[{"name":"Symbiosis Institute of Technology (SIT), Pune Campus, Symbiosis International (Deemed University) (SIU), Lavale, Pune 412115, Maharashtra, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5895-5350","authenticated-orcid":false,"given":"Sameer","family":"Sayyad","sequence":"additional","affiliation":[{"name":"Symbiosis Institute of Technology (SIT), Pune Campus, Symbiosis International (Deemed University) (SIU), Lavale, Pune 412115, Maharashtra, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9039-5733","authenticated-orcid":false,"given":"Javed","family":"Sayyad","sequence":"additional","affiliation":[{"name":"Symbiosis Institute of Technology (SIT), Pune Campus, Symbiosis International (Deemed University) (SIU), Lavale, Pune 412115, Maharashtra, India"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1016\/j.cogsys.2019.10.004","article-title":"Inception and ResNet Features are (Almost) Equivalent","volume":"59","author":"Beveridge","year":"2020","journal-title":"Cogn. Syst. Res."},{"key":"ref_2","first-page":"20","article-title":"Object Detection Based on Teachable Machine","volume":"7","author":"Mathew","year":"2021","journal-title":"J. VLSI Des. Signal Process."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Carney, M., Webster, B., Alvarado, I., Phillips, K., Howell, N., Griffith, J., Jongejan, J., Pitaru, A., and Chen, A. (2020, January 25\u201330). Teachable machine: Approachable web-based tool for exploring machine learning classification. Proceedings of the Conference on Human Factors in Computing Systems\u2014Proceedings, Association for Computing Machinery, Honolulu, HI, USA.","DOI":"10.1145\/3334480.3382839"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1145\/3167902.3167904","article-title":"Teachable machines for accessibility","volume":"119","author":"Kacorri","year":"2017","journal-title":"ACM SIGACCESS Access. Comput."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Salehi, A.W., Khan, S., Gupta, G., Alabduallah, B.I., Almjally, A., Alsolai, H., Siddiqui, T., and Mellit, A. (2023). A Study of CNN and Transfer Learning in Medical Imaging: Advantages, Challenges, Future Scope. Sustainability, 15.","DOI":"10.3390\/su15075930"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Kim, H.E., Cosa-Linan, A., Santhanam, N., Jannesari, M., Maros, M.E., and Ganslandt, T. (2022). Transfer learning for medical image classification: A literature review. BMC Med. Imaging, 22.","DOI":"10.1186\/s12880-022-00793-7"},{"key":"ref_7","first-page":"5857","article-title":"Simplifying Machine Learning: A Streamlit-Powered Interface for Rapid Model Development with PyCaret","volume":"12","author":"Kannan","year":"2024","journal-title":"Int. J. Innov. Res. Comput. Commun. Eng."},{"key":"ref_8","first-page":"25394","article-title":"Image Classification Using Transfer Learning and Deep Learning","volume":"10","author":"Desai","year":"2021","journal-title":"Int. J. Eng. Comput. Sci."},{"key":"ref_9","first-page":"1","article-title":"InceptionV3, ResNet50, ResNet18 and MobileNetV2 Performance Comparison on Face Recognition Classification","volume":"4","author":"Ariefwan","year":"2021","journal-title":"Literasi Nusant."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Hussain, M., Bird, J.J., and Faria, D.R. (2019). A study on CNN transfer learning for image classification. Advances in Intelligent Systems and Computing, Springer.","DOI":"10.1007\/978-3-319-97982-3_16"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Sheng, T., Feng, C., Zhuo, S., Zhang, X., Shen, L., and Aleksic, M. (2018, January 25). A Quantization-Friendly Separable Convolution for MobileNets. Proceedings of the 2018 1st Workshop on Energy Efficient Machine Learning and Cognitive Computing for Embedded Applications (EMC2), Williamsburg, VA, USA.","DOI":"10.1109\/EMC2.2018.00011"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018, January 18\u201323). MobileNetV2: Inverted Residuals and Linear Bottlenecks. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_13","unstructured":"Tan, M., and Le, Q.V. (2025, May 20). EfficientNetV2: Smaller Models and Faster Training. Available online: https:\/\/github.com\/google\/."},{"key":"ref_14","unstructured":"(2025, May 20). Deep Learning and Transfer Learning Approaches for Image Classification. Available online: https:\/\/www.researchgate.net\/publication\/333666150."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Dharavath, K., Amarnath, G., Talukdar, F.A., and Laskar, R.H. (2014, January 3\u20135). Impact of image preprocessing on face recognition: A comparative analysis. Proceedings of the 2014 International Conference on Communication and Signal Processing, Melmaruvathur, India.","DOI":"10.1109\/ICCSP.2014.6949918"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"32","DOI":"10.58496\/MJCSC\/2023\/005","article-title":"MobileNetV1-Based Deep Learning Model for Accurate Brain Tumor Classification","volume":"2023","author":"Mijwil","year":"2023","journal-title":"Mesopotamian J. Comput. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"206","DOI":"10.17135\/jdhs.2020.20.4.206","article-title":"Feasibility Study of Google\u2019s Teachable Machine in Diagnosis of Tooth-Marked Tongue","volume":"20","author":"Jeong","year":"2020","journal-title":"J. Dent. Hyg. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1007\/s44196-023-00397-1","article-title":"Transformative Breast Cancer Diagnosis using CNNs with Optimized ReduceLROnPlateau and Early Stopping Enhancements","volume":"17","author":"Mahesh","year":"2024","journal-title":"Int. J. Comput. Intell. Syst."},{"key":"ref_19","unstructured":"Ciresan, D.C., Meier, U., Masci, J., Maria Gambardella, L., and Schmidhuber, J. (2011, January 16\u201322). Flexible, High Performance Convolutional Neural Networks for Image Classification. Proceedings of the IJCAI Proceedings-International Joint Conference on Artificial Intelligence, Barcelona, Spain."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"6658058","DOI":"10.1155\/2021\/6658058","article-title":"COVID-19 Diagnosis Using an Enhanced Inception-ResNetV2 Deep Learning Model in CXR Images","volume":"2021","author":"Alruwaili","year":"2021","journal-title":"J. Healthc. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Dong, K., Zhou, C., Ruan, Y., and Li, Y. (2020, January 18\u201320). MobileNetV2 Model for Image Classification. Proceedings of the 2020 2nd International Conference on Information Technology and Computer Application, ITCA 2020, Guangzhou, China.","DOI":"10.1109\/ITCA52113.2020.00106"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"324","DOI":"10.35870\/ijsecs.v3i3.1784","article-title":"Revolutionizing Automotive Parts Classification Using InceptionV3 Transfer Learning","volume":"3","author":"Hindarto","year":"2023","journal-title":"Int. J. Softw. Eng. Comput. Sci. (IJSECS)"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Krishnapriya, S., and Karuna, Y. (2023). Pre-trained deep learning models for brain MRI image classification. Front. Hum. Neurosci., 17.","DOI":"10.3389\/fnhum.2023.1150120"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.5121\/ijdkp.2015.5201","article-title":"A Review on Evaluation Metrics for Data Classification Evaluations","volume":"5","author":"Hossin","year":"2015","journal-title":"Int. J. Data Min. Knowl. Manag. Process"},{"key":"ref_25","unstructured":"Didyk, L., Yarish, B., Beck, M.A., Bidinosti, C.P., and Henry, C.J. (2025, June 25). Strategies and Impact of Learning Curve Estimation for CNN-Based Image Classification. Available online: http:\/\/arxiv.org\/abs\/2310.08470."}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/17\/8\/370\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:28:05Z","timestamp":1760034485000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/17\/8\/370"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,15]]},"references-count":25,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["fi17080370"],"URL":"https:\/\/doi.org\/10.3390\/fi17080370","relation":{},"ISSN":["1999-5903"],"issn-type":[{"value":"1999-5903","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,15]]}}}