Original scientific paper
https://doi.org/10.1080/00051144.2024.2315405
Ensemble 3D CNN and U-Net-based brain tumour classification with MKKMC segmentation
Arul Venkatachalam
; Faculty of Information and Communication Engineering, Anna University, Chennai, India
*
Santhi Palanisamy
; Department of Computer Science and Engineering, Amrita School of Engineering, Chennai, India
Poongodi Chinnasamy
; Department of Computer Science and Engineering, Vivekanandha College of Engineering for Women (Autonomous), Namakkal, India
* Corresponding author.
Abstract
Advanced brain cancer is the deadliest type with just a few months survival rate. Existing technologies hinder the objective of forecasting cancer. This work aims to fulfil the pressing requirement for timely and precise identification of advanced-stage brain tumours, which are notorious for their markedly reduced life expectancy. It presents an innovative hybrid approach for predicting brain tumours and improves diagnostic capabilities. The Multiple Kernel K-Means Cluster
Algorithm (MKKCA) is used to segment brain MRI images effectively, differentiating healthy and
tumorous tissues. After segmentation, a hybrid approach with 3D-Convolutional Neural Network
(CNN) and U-Net has been utilized for classification. The objective is to effectively and accurately
distinguish normal and pathological brain images. To enhance the efficiency, we include the
Improved Whale Optimization Algorithm (IWOA), which guarantees accurate and dependable
performance via location updates. The methodology demonstrates outstanding precision with
98.5% accuracy rate, 98.56% specificity, 91% sensitivity, 87.45% precision and a recall rate of 96%
with the F-Measure at 96.02%. These findings, obtained using MATLAB, demonstrate a substantial performance improvement compared to current approaches. This development not only
represents a significant addition to diagnostic imaging but also a crucial role in the prediction
and treatment of brain cancers.
Keywords
Brain tumour; segmentation; hybrid approach; normal; abnormal; optimization; MRI data; greater performance
Hrčak ID:
326079
URI
Publication date:
15.2.2024.
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