Improved Deviation Sparse Fuzzy C‐Means‐2D Cumulative Sum Average Filter and Modified Sine Cosine Crow Search Algorithm‐Wavelet Extreme Learning Machine for Brain Tumor Detection and Classification

The brain tumor grows abnormally in the human brain, which causes brain cancer. Death rates have been rising annually for the past few decades due to negligence of early treatment of brain tumors. To reduce the death rate, early identification of tumors is crucial. Early brain tumor detection may potentially lower the risk of life. Manual tumor diagnosis is complex, challenging, and time‐consuming for medical professionals. Therefore, automatic detection and segmentation methods simplify the diagnosing procedure. Thus, automatic segmentation and classification methods are taken up to make the diagnosis process easy. This research proposes a two‐dimensional cumulative sum average filter (2D‐CSAF) for preprocessing images and an improved deviation sparse fuzzy C‐means (IDSFCM) with neighbor information for segmenting brain tumors from magnetic resonance images. The novel IDSFCM segmentation increases the noise reduction capability and enhances segmentation accuracies. The hybrid modified sine cosine algorithm‐crow search algorithm (MSCA‐CSA)–based WELM model is proposed to classify the brain tumor. The MSCA‐CSA algorithm optimizes the weights of the WELM model to increase the classification capability. The gray level co‐occurrence matrix (GLCM) feature extraction technique is employed to extract the features from the segmented images, and extracted features are given as input to the MSCA‐CSA‐WELM model for classification. The brain tumor dataset from Harvard Medical School is considered for this research. The proposed IDSFCM segmentation achieved 99.53% segmentation accuracy. The accuracy, specificity, and sensitivity performance measures are considered for the classification. The classification performance was evaluated using accuracy, sensitivity, and specificity metrics. The proposed MSCA‐CSA–based WELM model outperformed feature extraction–based classifiers, achieving 99.37% accuracy, 99.87% sensitivity, and 99.44% specificity during training.