{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,3]],"date-time":"2026-11-03T19:34:38Z","timestamp":1793734478765,"version":"4.6.1"},"reference-count":29,"publisher":"Termedia Sp. z.o.o.","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Pol J Radiol"],"abstract":"<jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Chronic liver disease (CLD) is a\u00a0significant health issue, and detection is crucial for effective treatment. This study aimed to develop a\u00a0deep learning based convolutional neural network (DeepCNN) to differentiate CLD from non-CLD patients using magnetic resonance imaging (MRI) images without segmentation, enhancing diagnostic accuracy and supporting timely intervention.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Material and methods<\/jats:title>\n                    <jats:p>A\u00a0retrospective study was conducted using MRI data from 184 patients collected between 2018 and 2024, totaling 1112 images (460 normal, 652 CLD). Various MRI sequences, including axial T1, T2, and coronal, were used. The images were preprocessed with resizing, augmentation, and normalization techniques. The DeepCNN model was trained and compared against traditional machine learning (ML) algorithms, including logistic regression, k-nearest neighbor, support vector machines, and random forest. Model performance was evaluated using accuracy, precision, recall, F1-score, and confusion matrices.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The DeepCNN model achieved a\u00a093% accuracy and an F1-score of 0.939. Precision and recall for CLD classification were 97% and 98%, respectively. In comparison, traditional ML algorithms performed with accuracies ranging from 72.31% to 83.16%, with random forest achieving the highest. The DeepCNN model significantly outperformed these methods, demonstrating its strength in medical image classification. Using axial-only images reduced accuracy to 86%, showing that coronal views contribute valuable information. Limitation of data constrained learning.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The DeepCNN model provides superior accuracy in diagnosing CLD compared to traditional ML me\u00adthods, using MRI images without segmentation. This approach offers a\u00a0practical solution for improving CLD detection and paves the way for future enhancements using attention mechanisms and advanced deep learning architectures.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.5114\/pjr\/210254","type":"journal-article","created":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T11:11:54Z","timestamp":1768475514000},"source":"Crossref","is-referenced-by-count":2,"title":["Chronic liver disease detection using deep convolutional neural networks with MRI data: a\u00a0deep learning approach"],"prefix":"10.5114","volume":"91","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-0432-2176","authenticated-orcid":true,"given":"Elif","family":"ZORO\u011eLU ALTINKAYA","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3172-1591","authenticated-orcid":false,"given":"Emre","family":"ALTINKAYA","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5989-1897","authenticated-orcid":true,"given":"Emre","family":"Emekli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1729-6958","authenticated-orcid":false,"given":"elif","family":"g\u00fcndo\u011fdu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"3061","published-online":{"date-parts":[[2026,1,15]]},"reference":[{"key":"ref-1649754","unstructured":"Kalra A, Yetiskul E, Wehrle CJ, Tuma F. 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