Brain Tumor Classification Using Meta-Heuristic Optimized Convolutional Neural Networks

• Published in: Journal of personalized medicine Vol. 13; no. 2; p. 181
• Main Authors: Kurdi, Sarah Zuhair, Ali, Mohammed Hasan, Jaber, Mustafa Musa, Saba, Tanzila, Rehman, Amjad, Damaševičius, Robertas
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 20-01-2023; MDPI
• Subjects: Accuracy; Algorithms; Brain cancer; Brain tumors; Classification; Development and progression; Hawks; Image processing; Magnetic resonance imaging; Mathematical optimization; Medical imaging equipment; Methods; Neural networks; Optimization algorithms; Optimization techniques; Precision medicine; Tomography; Tumors; Wavelet transforms; Iraq; deep learning; brain tumor; Harris Hawks optimization; health risks; MRI; healthcare
• ISSN: 2075-4426; 2075-4426
• DOI: 10.3390/jpm13020181

The field of medical image processing plays a significant role in brain tumor classification. The survival rate of patients can be increased by diagnosing the tumor at an early stage. Several automatic systems have been developed to perform the tumor recognition process. However, the existing systems could be more efficient in identifying the exact tumor region and hidden edge details with minimum computation complexity. The Harris Hawks optimized convolution network (HHOCNN) is used in this work to resolve these issues. The brain magnetic resonance (MR) images are pre-processed, and the noisy pixels are eliminated to minimize the false tumor recognition rate. Then, the candidate region process is applied to identify the tumor region. The candidate region method investigates the boundary regions with the help of the line segments concept, which reduces the loss of hidden edge details. Various features are extracted from the segmented region, which is classified by applying a convolutional neural network (CNN). The CNN computes the exact region of the tumor with fault tolerance. The proposed HHOCNN system was implemented using MATLAB, and performance was evaluated using pixel accuracy, error rate, accuracy, specificity, and sensitivity metrics. The nature-inspired Harris Hawks optimization algorithm minimizes the misclassification error rate and improves the overall tumor recognition accuracy to 98% achieved on the Kaggle dataset.