Szczegóły publikacji
Opis bibliograficzny
Enhancing breast cancer diagnosis: a CNN-based approach for medical image segmentation and classification / Shoffan SAIFULLAH, Rafał DREŻEWSKI // W: Computational Science – ICCS 2024 : 24th International Conference : Malaga, Spain, July 2–4, 2024 : proceedings, Pt. 4 / eds. Leonardo Franco, [et al.]. — Cham : Springer, cop. 2024. — (Lecture Notes in Computer Science ; ISSN 0302-9743 ; LNCS 14835). — ISBN: 978-3-031-63771-1; e-ISBN: 978-3-031-63772-8. — S. 155–162. — Bibliogr., Abstr. — Publikacja dostępna online od: 2024-06-28. — S. Saifullah – dod. afiliacja: Universitas Pembangunan Nasional Veteran Yogyakarta, Indonesia
Autorzy (2)
Dane bibliometryczne
| ID BaDAP | 154122 |
|---|---|
| Data dodania do BaDAP | 2024-07-03 |
| DOI | 10.1007/978-3-031-63772-8_15 |
| Rok publikacji | 2024 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | International Conference on Computational Science 2024 |
| Czasopismo/seria | Lecture Notes in Computer Science |
Abstract
This study introduces a novel Convolutional Neural Network (CNN) approach for breast cancer diagnosis, which seamlessly integrates segmentation and classification. The segmentation process achieves high precision, with Jaccard Index (JI) values of 0.89, 0.92, and 0.87 for Normal, Benign, and Malignant regions, respectively, resulting in an overall JI of 0.896. Similarly, the Dice Similarity Coefficient (DSC) values are notably high, with 0.94, 0.96, and 0.92 for the corresponding regions, yielding an overall DSC of 0.943. The CNN model exhibits high accuracy, specificity, precision, recall, and F1 score across all classes, establishing its reliability for clinical applications. This research comprehensively evaluates the model’s performance metrics, addressing challenges in breast cancer diagnostics and proposing an innovative CNN-based solution. Beyond immediate applications, it lays a robust foundation for future medical imaging advancements, enhancing diagnostic accuracy and patient outcomes.
