{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:31:59Z","timestamp":1760146319322,"version":"build-2065373602"},"reference-count":40,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T00:00:00Z","timestamp":1729468800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"University of the Basque Country (UPV\/EHU)","award":["GIU23\/022","PID2021-126701OB-I00"],"award-info":[{"award-number":["GIU23\/022","PID2021-126701OB-I00"]}]},{"name":"Ministerio de Ciencia, Innovaci\u00f3n y Universidades, AEI","award":["GIU23\/022","PID2021-126701OB-I00"],"award-info":[{"award-number":["GIU23\/022","PID2021-126701OB-I00"]}]},{"name":"ERDF A way of making Europe","award":["GIU23\/022","PID2021-126701OB-I00"],"award-info":[{"award-number":["GIU23\/022","PID2021-126701OB-I00"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>The exceptional performance of ImageNet competition winners in image classification has led AI researchers to repurpose these models for a whole range of tasks using transfer learning (TL). TL has been hailed for boosting performance, shortening learning time and reducing computational effort. Despite these benefits, issues such as data sparsity and the misrepresentation of classes can diminish these gains, occasionally leading to misleading TL accuracy scores. This research explores the innovative concept of endowing ImageNet models with a self-awareness that enables them to recognize their own accumulated knowledge and experience. Such self-awareness is expected to improve their adaptability in various domains. We conduct a case study using two different datasets, PBC and BCCD, which focus on blood cell classification. The PBC dataset provides high-resolution images with abundant data, while the BCCD dataset is hindered by limited data and inferior image quality. To compensate for these discrepancies, we use data augmentation for BCCD and undersampling for both datasets to achieve balance. Subsequent pre-processing generates datasets of different size and quality, all geared towards blood cell classification. We extend conventional evaluation tools with novel metrics\u2014\u201caccuracy difference\u201d and \u201closs difference\u201d\u2014to detect overfitting or underfitting and evaluate their utility as potential indicators for learning behavior and promoting the self-confidence of ImageNet models. Our results show that these metrics effectively track learning progress and improve the reliability and overall performance of ImageNet models in new applications. This study highlights the transformative potential of turning ImageNet models into self-aware entities that significantly improve their robustness and efficiency in various AI tasks. This groundbreaking approach opens new perspectives for increasing the effectiveness of transfer learning in real-world AI implementations.<\/jats:p>","DOI":"10.3390\/make6040118","type":"journal-article","created":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T07:53:43Z","timestamp":1729583623000},"page":"2400-2421","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Towards Self-Conscious AI Using Deep ImageNet Models: Application for Blood Cell Classification"],"prefix":"10.3390","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2887-8232","authenticated-orcid":false,"given":"Mohamad","family":"Abou Ali","sequence":"first","affiliation":[{"name":"Department of Computer Science and Artificial Intelligence, University of the Basque Country (UPV\/EHU), Manuel Lardizabal, 1, 20018 San Sebastian, Spain"},{"name":"Department of Biomedical Engineering, Lebanese International University (LIU), Salim Salam, Mouseitbeh, Beirut 146404, Lebanon"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6581-9680","authenticated-orcid":false,"given":"Fadi","family":"Dornaika","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Artificial Intelligence, University of the Basque Country (UPV\/EHU), Manuel Lardizabal, 1, 20018 San Sebastian, Spain"},{"name":"IKERBASQUE, Basque Foundation for Science, Plaza Euskadi, 5, 48009 Bilbao, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0229-5722","authenticated-orcid":false,"given":"Ignacio","family":"Arganda-Carreras","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Artificial Intelligence, University of the Basque Country (UPV\/EHU), Manuel Lardizabal, 1, 20018 San Sebastian, Spain"},{"name":"IKERBASQUE, Basque Foundation for Science, Plaza Euskadi, 5, 48009 Bilbao, Spain"},{"name":"Donostia International Physics Center (DIPC), Manuel Lardizabal, 4, 20018 San Sebastian, Spain"},{"name":"Biofisika Institute (CSIC, UPV\/EHU), Barrio Sarriena s\/n, 48940 Leioa, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1007\/s42979-021-00592-x","article-title":"Machine learning: Algorithms, real-world applications and research directions","volume":"2","author":"Sarker","year":"2021","journal-title":"SN Comput. 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