Szczegóły publikacji
Opis bibliograficzny
Brain MRI detection and classification: Harnessing convolutional neural networks and multi-level thresholding / Rasool Reddy Kamireddy, Rajesh N. V. P. S. Kandala, Ravindra Dhuli, Srinivasu Polinati, Kamesh Sonti, Ryszard TADEUSIEWICZ, Paweł Pławiak // PLoS One [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 1932-6203. — 2024 — vol. 19 iss. 8 art. no. e0306492, s. 1–25. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 23–25, Abstr. — Publikacja dostępna online od: 2024-08-01
Autorzy (7)
- Kamireddy Rasool Reddy
- Kandala Rajesh N.V.P.S.
- Dhuli Ravindra
- Polinati Srinivasu
- Sonti Kamesh
- AGHTadeusiewicz Ryszard
- Pławiak Paweł
Dane bibliometryczne
| ID BaDAP | 155209 |
|---|---|
| Data dodania do BaDAP | 2024-09-11 |
| Tekst źródłowy | URL |
| DOI | 10.1371/journal.pone.0306492 |
| Rok publikacji | 2024 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | PLoS One |
Abstract
Brain tumor detection in clinical applications is a complex and challenging task due to the intricate structures of the human brain. Magnetic Resonance (MR) imaging is widely preferred for this purpose because of its ability to provide detailed images of soft brain tissues, including brain tissue, cerebrospinal fluid, and blood vessels. However, accurately detecting brain tumors from MR images remains an open problem for researchers due to the variations in tumor characteristics such as intensity, texture, size, shape, and location. To address these issues, we propose a method that combines multi-level thresholding and Convolutional Neural Networks (CNN). Initially, we enhance the contrast of brain MR images using intensity transformations, which highlight the infected regions in the images. Then, we use the suggested CNN architecture to classify the enhanced MR images into normal and abnormal categories. Finally, we employ multi-level thresholding based on Tsallis entropy (TE) and differential evolution (DE) to detect tumor region(s) from the abnormal images. To refine the results, we apply morphological operations to minimize distortions caused by thresholding. The proposed method is evaluated using the widely used Harvard Medical School (HMS) dataset, and the results demonstrate promising performance with 99.5% classification accuracy and 92.84% dice similarity coefficient. Our approach outperforms existing state-of-the-art methods in brain tumor detection and automated disease diagnosis from MR images.
