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MRI Brain tumor segmentation and classification with improved U-Net model

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Abstract

Brain tumors are among the deadliest diseases in the world. Early diagnosis thereby improves the patient's prospects and likelihood of recovery. It takes a lot of work to separate cancer from other brain abnormalities from MRI images. Various approaches have been devised to predict and divide the tumor. Choosing the finest feature extractor, the long run-time need, and the necessity for expert support are just a few of the challenges they encounter. Improved U-Net and a White Shark aided Beluga Whale Optimization based DCNN are introduced to segment and classify brain tumors (WSBWO based DCNN) into four stages in order to address such concerns. This work uses an improved U-Net based image segmentation model, and the first step of MF based preprocessing uses the input image. The third stage is feature extraction, when statistical features such as I-GBP and MTH are extracted. Subsequently, the DCNN classification system trained on WSBWO is used to classify brain tumors. To validate the suggested work over alternative approaches, various studies were finally carried out.

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Data availability

The dataset is BraTS, available at https://www.kaggle.com/datasets/dschettler8845/brats-2021-task1.

Abbreviations

MRI:

Magnetic Resonance Imaging

KNN:

K-Nearest Neighbor

SVM:

Support Vector Machine

DT:

Decision Tree

CNN:

Convolutional Neural Network

GLCM:

Gray-Level Co-Occurrence Matrix

FCM:

Fuzzy C-Means

GHFC:

Gaussian Hybrid Fuzzy Clustering

RBNN:

Radial Basis Neural Network

MRG:

Modified Region Growing

GOA:

Grasshopper Optimization Algorithm

ASVM:

Adaptive Support Vector Machine

BAFCOM:

Bat Algorithm with Fuzzy C-Ordered Means

ECN:

Enhanced Capsule Networks

ANN:

Artificial Neural Network

DNN:

Deep Neural Network

KPCA:

Kernel principal component analysis

MLE:

Maximum Likelihood Estimation

DL:

Deep Learning

ROI:

Region Of Interest

ML :

Machine Learning

BCE:

Binary Cross Entropy

MF:

Median Filtering

MTH:

Multi-Texton Histogram

SD:

Standard Deviation

DCNN:

Deep Convolutional Neural Network

RF:

Random Forest

NRA:

Noise Removal Algorithm

RNN:

Recurrent Neural Network

LSTM:

Long Short Term Memory

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Jawaharlal Nehru Technological University Anantapur, Ananthapuramu-515002, Andhra Pradesh, India

    Palleti Venkata Kusuma & S. Chandra Mohan Reddy

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  1. Palleti Venkata Kusuma
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  2. S. Chandra Mohan Reddy
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Correspondence to Palleti Venkata Kusuma.

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Kusuma, P.V., Reddy, S.C.M. MRI Brain tumor segmentation and classification with improved U-Net model. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18893-7

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