Academic Journal
A Novel Hybrid Deep Learning Model with Receptive Field-Enhanced Skip Connections and Adaptive Loss for Medical Image Segmentation.
| Τίτλος: | A Novel Hybrid Deep Learning Model with Receptive Field-Enhanced Skip Connections and Adaptive Loss for Medical Image Segmentation. |
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| Συγγραφείς: | Zarrin, Mahdi, Nikkhah, Haniyeh |
| Πηγή: | Journal of Artificial Intelligence & Data Mining (JAIDM); Apr2026, Vol. 14 Issue 2, p235-256, 23p |
| Θεματικοί όροι: | Deep learning, Diagnostic imaging, Feature extraction, Loss functions (Statistics), Dermatology |
| Περίληψη: | Medical image analysis, crucial for disease diagnosis and treatment, often suffers from the challenge of class imbalance, where normal tissue significantly outweighs abnormal regions in terms of area. Furthermore, varying class ratios across different images within a dataset complicate the application of uniform loss adjustments. To address these issues and advance automated segmentation, this study proposes a novel deep learning model that integrates the strengths of YOLO Version 8's efficient feature extraction modules (SPPF and C2F) within a U-shaped architecture enhanced by a Receptive Field Enhancement (RFE) module. The RFE module, acting as an advanced skip connection, strategically fuses multi-scale features from corresponding and subsequent encoder layers processed through SPPF and C2F to enrich feature transfer and improve receptive field. To specifically tackle the class imbalance and the diversity of class distributions across images, we introduce a novel Adapt Exponential Loss function. This pixel-level loss dynamically adjusts class weights for each image based on its individual lesion-to-total-pixel ratio (k). We evaluated our proposed model and loss function on challenging skin lesion datasets: ISIC 2018, ISIC 2017, and PH2. Our method achieved significant segmentation performance with IoU scores of 86.47%, 85.67%, and 93.13%, and Dice scores of 91.63%, 90.19%, and 96.02% on ISIC 2018, ISIC 2017, and PH2, respectively, demonstrating its effectiveness in accurately delineating skin lesions despite class imbalance and varying lesion proportions. This work contributes a robust framework for medical image segmentation, facilitating more reliable diagnostic tools in dermatology. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Artificial Intelligence & Data Mining (JAIDM) is the property of Shahrud University of Technology and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:edb&genre=article&issn=23225211&ISBN=&volume=14&issue=2&date=20260401&spage=235&pages=235-257&title=Journal of Artificial Intelligence & Data Mining (JAIDM)&atitle=A%20Novel%20Hybrid%20Deep%20Learning%20Model%20with%20Receptive%20Field-Enhanced%20Skip%20Connections%20and%20Adaptive%20Loss%20for%20Medical%20Image%20Segmentation.&aulast=Zarrin%2C%20Mahdi&id=DOI:10.22044/jadm.2025.16467.2771 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
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| Items | – Name: Title Label: Title Group: Ti Data: A Novel Hybrid Deep Learning Model with Receptive Field-Enhanced Skip Connections and Adaptive Loss for Medical Image Segmentation. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zarrin%2C+Mahdi%22">Zarrin, Mahdi</searchLink><br /><searchLink fieldCode="AR" term="%22Nikkhah%2C+Haniyeh%22">Nikkhah, Haniyeh</searchLink> – Name: TitleSource Label: Source Group: Src Data: Journal of Artificial Intelligence & Data Mining (JAIDM); Apr2026, Vol. 14 Issue 2, p235-256, 23p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Diagnostic+imaging%22">Diagnostic imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Loss+functions+%28Statistics%29%22">Loss functions (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Dermatology%22">Dermatology</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Medical image analysis, crucial for disease diagnosis and treatment, often suffers from the challenge of class imbalance, where normal tissue significantly outweighs abnormal regions in terms of area. Furthermore, varying class ratios across different images within a dataset complicate the application of uniform loss adjustments. To address these issues and advance automated segmentation, this study proposes a novel deep learning model that integrates the strengths of YOLO Version 8's efficient feature extraction modules (SPPF and C2F) within a U-shaped architecture enhanced by a Receptive Field Enhancement (RFE) module. The RFE module, acting as an advanced skip connection, strategically fuses multi-scale features from corresponding and subsequent encoder layers processed through SPPF and C2F to enrich feature transfer and improve receptive field. To specifically tackle the class imbalance and the diversity of class distributions across images, we introduce a novel Adapt Exponential Loss function. This pixel-level loss dynamically adjusts class weights for each image based on its individual lesion-to-total-pixel ratio (k). We evaluated our proposed model and loss function on challenging skin lesion datasets: ISIC 2018, ISIC 2017, and PH2. Our method achieved significant segmentation performance with IoU scores of 86.47%, 85.67%, and 93.13%, and Dice scores of 91.63%, 90.19%, and 96.02% on ISIC 2018, ISIC 2017, and PH2, respectively, demonstrating its effectiveness in accurately delineating skin lesions despite class imbalance and varying lesion proportions. This work contributes a robust framework for medical image segmentation, facilitating more reliable diagnostic tools in dermatology. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Journal of Artificial Intelligence & Data Mining (JAIDM) is the property of Shahrud University of Technology and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.22044/jadm.2025.16467.2771 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 23 StartPage: 235 Subjects: – SubjectFull: Deep learning Type: general – SubjectFull: Diagnostic imaging Type: general – SubjectFull: Feature extraction Type: general – SubjectFull: Loss functions (Statistics) Type: general – SubjectFull: Dermatology Type: general Titles: – TitleFull: A Novel Hybrid Deep Learning Model with Receptive Field-Enhanced Skip Connections and Adaptive Loss for Medical Image Segmentation. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zarrin, Mahdi – PersonEntity: Name: NameFull: Nikkhah, Haniyeh IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 23225211 Numbering: – Type: volume Value: 14 – Type: issue Value: 2 Titles: – TitleFull: Journal of Artificial Intelligence & Data Mining (JAIDM) Type: main |
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