Development of a Knowledge-Based Multimodal Deep Learning System for Automatic Breast Lesion Segmentation and Diagnosis in MG/DMR Images

Araz N., Orhan G., Cavusoglu M., Surmeli H. E., BAYRAM B., Cakar T.

8th International Conference on Computer Science and Engineering, UBMK 2023, Burdur, Türkiye, 13 - 15 Eylül 2023, ss.578-583 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/ubmk59864.2023.10286633
  • Basıldığı Şehir: Burdur
  • Basıldığı Ülke: Türkiye
  • Sayfa Sayıları: ss.578-583
  • Anahtar Kelimeler: breast lesion segmentation, computer-aided diagnosis, Deep learning networks, domestic database creation, multimodal image processing
  • Yıldız Teknik Üniversitesi Adresli: Evet

Özet

Deep learning networks (DLNs) rely on labeled training datasets as their fundamental building blocks. While various databases exist worldwide, there is currently no domestic solution available in our country. This project aims to create a domestic database by automatically segmenting breast lesions in MG/DMR images based on their types and developing a knowledge-based multimodal DL-based integrated computer-aided diagnosis system to analyze the images, thereby providing the system with continuous learning capability. Different brands of devices exist for MG/DMR, necessitating the multimodal operation of image processing/artificial intelligence algorithms. To achieve this goal, the network was trained first, and then prelearned data were transferred to enable the training of data from different networks once accurate results are obtained. The developed system has the potential to enable the automatic detection of breast lesions, ensuring fast and high diagnostic accuracy. Additionally, it might also facilitate the retrospective analysis of patients' periodic check-up results.