{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T13:22:51Z","timestamp":1775913771411,"version":"3.50.1"},"reference-count":34,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,12,14]],"date-time":"2024-12-14T00:00:00Z","timestamp":1734134400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Convolutional Neural Networks (CNNs) are the prevalent technology in computer vision and have become increasingly popular for medical imaging data classification and analysis. In this field, due to the scarcity of medical data, pretrained ResNets on ImageNet can be considered a suitable first approach. This paper examines the medical imaging classification accuracy of conventional basic custom CNNs compared to ImageNet pretrained ResNets on various medical datasets in an effort to give more information about the importance of medical data and its preprocessing techniques for disease studies. Microscope-extracted cytological images were examined along with chest X-rays, MRI brain scans, and melanoma photographs. The medical images were examined in various sets, class combinations, and resolutions. Augmented image datasets and asymmetrical training and validation splits among the classes were also examined. Models were developed after they were tested and fine-tuned with respect to their network size, parameter values and network methods, image resolution, size of dataset, multitude, and genre of class. Overfitting was also examined, and comparative studies regarding the computational cost of different models were performed. The models achieved high accuracy in image classification that varies depending on the dataset and can be easily incorporated in future over-the-internet medical decision-supporting (telemedicine) environments. In addition, it appeared that conventional basic custom CNN overperformed ImageNet pretrained ResNets. The obtained results indicate the importance of utilizing medical image data as a testbed for improvements in CNN classification performance and the possibility of using CNNs and data preprocessing techniques for disease studies.<\/jats:p>","DOI":"10.3390\/info15120806","type":"journal-article","created":{"date-parts":[[2024,12,17]],"date-time":"2024-12-17T05:26:02Z","timestamp":1734413162000},"page":"806","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Comparative Analysis of Conventional CNN v\u2019s ImageNet Pretrained ResNet in Medical Image Classification"],"prefix":"10.3390","volume":"15","author":[{"given":"Christos","family":"Raptis","sequence":"first","affiliation":[{"name":"Medical Physics Laboratory, Department of Medicine, Democritus University of Thrace, 681 00 Alexandroupolis, Greece"},{"name":"School of Science and Technology, Hellenic Open University, 263 31 Patra, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Efstratios","family":"Karavasilis","sequence":"additional","affiliation":[{"name":"Medical Physics Laboratory, Department of Medicine, Democritus University of Thrace, 681 00 Alexandroupolis, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6604-1110","authenticated-orcid":false,"given":"George","family":"Anastasopoulos","sequence":"additional","affiliation":[{"name":"Medical Informatics Laboratory, Department of Medicine, Democritus University of Thrace, 681 00 Alexandroupolis, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6760-3971","authenticated-orcid":false,"given":"Adam","family":"Adamopoulos","sequence":"additional","affiliation":[{"name":"Medical Physics Laboratory, Department of Medicine, Democritus University of Thrace, 681 00 Alexandroupolis, Greece"},{"name":"School of Science and Technology, Hellenic Open University, 263 31 Patra, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Alexander, R.G., Yazdanie, F., Waite, S., Chaudhry, Z.A., Kolla, S., Macknik, S.L., and Martinez-Conde, S. 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