Skripsi

SEGMENTASI SEL TUMOR OTAK MENGGUNAKAN MODIFIKASI ARSITEKTUR 3D U-NET INCEPTION PADA CITRA MAGNETIC RESONANCE IMAGING OTAK

---

Penilaian

---

Brain tumor cells can be identified by applying image segmentation techniques using Convolutional Neural Network (CNN). The CNN architecture that is often used in 3-dimensional image segmentation is 3D U-Net. In this study, the implementation of 3D U-Net Inception architecture was carried out which is a modification of 3D U-Net and Inception architecture for brain tumor cell segmentation on brain Magnetic Resonance Imaging (MRI) images. The stages of research carried out are data collection, data preprocessing, training, testing, evaluation, analysis and interpretation of results, and conclusion making. The results of the study using the Brain Tumor Segmentation dataset were the values of accuracy, sensitivity, specificity, Intersection over Union (IoU), and f1-score respectively of 99.04%, 83.68%, 93.73%, 70.86%, and 81.94%. Based on the results obtained, it can be said that the 3D U-Net Inception architecture is able to segment brain tumor cells on brain MRI images, but still has a fairly low IoU value.

---

Availability

Inventory Code	Barcode	Call Number	Location	Status
2407002449	T142828	T1428282024	Central Library (References)	Available but not for loan - Not for Loan

Detail Information

Series Title

-

Call Number

T1428282024

Publisher

Inderalaya : Prodi Ilmu Matematika, Fakultas Matematika dan Ilmu Pengetahuan Alam., 2024

Collation

xii, 61 hlm.; ilus.; 29 cm.

Language

Indonesia

ISBN/ISSN

-

Classification

616.990 7

Content Type

Text

Media Type

-

Carrier Type

-

Edition

-

Subject(s)

Prodi Ilmu Matematika
Sel Tumor Otak

Specific Detail Info

-

Statement of Responsibility

MI

Other version/related

File Attachment

Comments

---

You must be logged in to post a comment