Application of Genetic Algorithm and U-Net in Brain Tumor Segmentation and Classification: A Deep Learning Approach.

Autor: Arif M; Department of Computer Science, Superior University, Lahore, Pakistan., Jims A; Department of Computer Science and Information Technology JAIN (Deemed-to-be University), Bangalore, India., F A; Department of Computer Science and Engineering, Sree Buddha College of Engineering, Pattoor Alappuzha, Kerala, India., Geman O; Stefan Cel Mare University of Suceava Romania, Suceava, Romania., Craciun MD; Stefan Cel Mare University of Suceava Romania, Suceava, Romania., Leuciuc F; Stefan Cel Mare University of Suceava Romania, Suceava, Romania.
Jazyk: angličtina
Zdroj: Computational intelligence and neuroscience [Comput Intell Neurosci] 2022 Sep 15; Vol. 2022, pp. 5625757. Date of Electronic Publication: 2022 Sep 15 (Print Publication: 2022).
DOI: 10.1155/2022/5625757
Abstrakt: The development of unusual cells in the cerebrum causes brain cancer. It is classified primarily into two classes: a noncarcinogenic (benign) type of growth and cancerous (malignant) growth. Early detection of this disease is a quintessential task for all medical practice professionals. For traditional approaches of tumor detections, certain limitations exist. They include less effectiveness, inability to detect due to low-quality processing of images, less dataset for training and testing, less predictive nature to models, and skipping of quintessential stages. All these lead to inaccurate results of tumor detections. To overcome this issue, this paper brings an effective deep learning technique for brain tumor detection with the following stages: (a) data collection from REMBRANDT dataset containing multisequence MRI of 130 patients; (b) preprocessing using conversion to greyscale, skull stripping, and histogram equalization; (c) segmentation uses genetic algorithm; (d) feature extraction using discrete wavelet transform (DWT); (e) particle swarm optimization technique for feature selection; (f) classification using U-Net. Experiment evaluation states that the proposed model (GA-UNET) outperforms (accuracy: 0.97, sensitivity: 0.98, specificity: 0.98) compared to other advanced models.
Competing Interests: The authors declare that they have no conflicts of interest.
(Copyright © 2022 Muhammad Arif et al.)
Databáze: MEDLINE
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